From baac29ab7afbd6c7b66598b8cffed67ad2bbd6be Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Fri, 3 May 2024 17:45:29 +0000 Subject: [PATCH] build based on b2bcebe --- dev/.documenter-siteinfo.json | 2 +- dev/api/index.html | 30 +++++++++++++------------- dev/assets/themes/documenter-dark.css | 2 +- dev/assets/themes/documenter-light.css | 2 +- dev/customization/index.html | 24 ++++++++++----------- dev/examples/index.html | 2 +- dev/function_reference/index.html | 2 +- dev/index.html | 2 +- dev/keep_drop_order/index.html | 2 +- dev/regression_statistics/index.html | 22 +++++++++---------- dev/search_index.js | 2 +- 11 files changed, 46 insertions(+), 46 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index efc147a..0010dc8 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-03-18T22:01:18","documenter_version":"1.3.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-03T17:45:26","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index e066bde..cc9c4af 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -45,7 +45,7 @@ -------------------- -------------------- -------------------- julia> DataRow(["", "Group 1" => 2:3, "Group 2" => 4:5]; render=LatexTable()) - & \multicolumn{2}{r}{Group 1} & \multicolumn{2}{r}{Group 2} \\ source

RegressionTable

RegressionTables.RegressionTable — Type
mutable struct RegressionTable{T<:AbstractRenderType} <: AbstractMatrix{String}
+ & \multicolumn{2}{r}{Group 1} & \multicolumn{2}{r}{Group 2} \\
source

RegressionTable

RegressionTables.RegressionTable — Type
mutable struct RegressionTable{T<:AbstractRenderType} <: AbstractMatrix{String}
     data::Vector{DataRow{T}}
     align::String
     render::T
@@ -94,27 +94,27 @@
 SepalWidth               3.057   0.436   2.800    3.000   3.300
 PetalLength              3.758   1.765   1.600    4.350   5.100
 PetalWidth               1.199   0.762   0.300    1.300   1.800
----------------------------------------------------------------
source

Render Types

RegressionTables.AbstractRenderType — Type
abstract type AbstractRenderType end

The generic top level type for render types. Most defaults are set here unless there is a reason it needs to be set at a lower level. Subtypes are still abstract types and include AbstractAscii, AbstractLatex, and AbstractHtml.

source

Ascii

RegressionTables.AbstractAscii — Type
abstract type AbstractAscii <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractAscii, so new tables (with different defaults) can be created by subtyping AbstractAscii with minimal effort.

source
RegressionTables.AsciiTable — Type
struct AsciiTable <: AbstractAscii end

The main concrete type for AbstractAscii. This is the default type used for plain text rendering.

source

Latex

RegressionTables.AbstractLatex — Type
abstract type AbstractLatex <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractLatex, so new tables (with different defaults) can be created by subtyping AbstractLatex with minimal effort.

source
RegressionTables.LatexTable — Type
struct LatexTable <: AbstractLatex end

The main concrete type for AbstractLatex. This type is used to create Latex tables.

source
RegressionTables.LatexTableStar — Type
struct LatexTableStar <: AbstractLatex end

An alternative concrete type for AbstractLatex. This type is used to create Latex tables that span the entire text width.

source

HTML

RegressionTables.AbstractHtml — Type
abstract type AbstractHtml <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractHtml, so new tables (with different defaults) can be created by subtyping AbstractHtml with minimal effort.

source
RegressionTables.HtmlTable — Type
struct HtmlTable <: AbstractHtml end

The main concrete type for AbstractHtml. This type is used to create HTML tables.

source

Coefficient Naming

RegressionTables.AbstractCoefName — Type
abstract type AbstractCoefName end

These names largely mirror their equivalents in StatsModels.jl. The main difference here is that the names are always based on strings (instead of symbols). There are also several default functions that are resused (e.g., get and replace) to make relabeling coefficients easier.

AbstractCoefName simply acts as a parent type to the other types. The other types are:

Using the function get_coefname will return the appropriate type for the term.

source
RegressionTables.get_coefname — Function
get_coefname(x::AbstractTerm)::CoefName
+---------------------------------------------------------------
source

Render Types

RegressionTables.AbstractRenderType — Type
abstract type AbstractRenderType end

The generic top level type for render types. Most defaults are set here unless there is a reason it needs to be set at a lower level. Subtypes are still abstract types and include AbstractAscii, AbstractLatex, and AbstractHtml.

source

Ascii

RegressionTables.AbstractAscii — Type
abstract type AbstractAscii <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractAscii, so new tables (with different defaults) can be created by subtyping AbstractAscii with minimal effort.

source
RegressionTables.AsciiTable — Type
struct AsciiTable <: AbstractAscii end

The main concrete type for AbstractAscii. This is the default type used for plain text rendering.

source

Latex

RegressionTables.AbstractLatex — Type
abstract type AbstractLatex <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractLatex, so new tables (with different defaults) can be created by subtyping AbstractLatex with minimal effort.

source
RegressionTables.LatexTable — Type
struct LatexTable <: AbstractLatex end

The main concrete type for AbstractLatex. This type is used to create Latex tables.

source
RegressionTables.LatexTableStar — Type
struct LatexTableStar <: AbstractLatex end

An alternative concrete type for AbstractLatex. This type is used to create Latex tables that span the entire text width.

source

HTML

RegressionTables.AbstractHtml — Type
abstract type AbstractHtml <: AbstractRenderType end

The abstract type for most plain text rendering. Printing is defined using the AbstractHtml, so new tables (with different defaults) can be created by subtyping AbstractHtml with minimal effort.

source
RegressionTables.HtmlTable — Type
struct HtmlTable <: AbstractHtml end

The main concrete type for AbstractHtml. This type is used to create HTML tables.

source

Coefficient Naming

RegressionTables.AbstractCoefName — Type
abstract type AbstractCoefName end

These names largely mirror their equivalents in StatsModels.jl. The main difference here is that the names are always based on strings (instead of symbols). There are also several default functions that are resused (e.g., get and replace) to make relabeling coefficients easier.

AbstractCoefName simply acts as a parent type to the other types. The other types are:

Using the function get_coefname will return the appropriate type for the term.

source
RegressionTables.get_coefname — Function
get_coefname(x::AbstractTerm)::CoefName
 get_coefname(x::Term)::CoefName
 get_coefname(x::InteractionTerm)::InteractedCoefName
 get_coefname(x::InterceptTerm{H})::InterceptCoefName
-get_coefname(x::CategoricalTerm)::CategoricalCoefName
source
RegressionTables.CategoricalCoefName — Type
struct CategoricalCoefName <: AbstractCoefName
+get_coefname(x::CategoricalTerm)::CategoricalCoefName
source
RegressionTables.CategoricalCoefName — Type
struct CategoricalCoefName <: AbstractCoefName
     name::String
     level::String
 end

Used to store the name of a coefficient for a CategoricalTerm. The level is the level of the categorical. In other words, the name is the column name, the level is the category within that column.

In a regression, the display of categorical terms is typically displayed as "name: level". You can change how the categorical term is "equal" by changing the categorical_equal function. The default is ": ", but you can change: this by setting:

RegressionTables.categorical_equal(render::AbstractRenderType) = " = "
 RegressionTables.categorical_equal(render::AbstractLatex) = " $=$ "

You can also change how the categorical term is displayed by changing the repr function. The default is:

Base.repr(render::AbstractRenderType, x::RegressionTables.CategoricalCoefName; args...) =
-    "$(RegressionTables.value(x))$(RegressionTables.categorical_equal(render)) $(x.level)"
source
RegressionTables.CoefName — Type
struct CoefName <: AbstractCoefName
+    "$(RegressionTables.value(x))$(RegressionTables.categorical_equal(render)) $(x.level)"
source
RegressionTables.CoefName — Type
struct CoefName <: AbstractCoefName
     name::String
-end

Used to store the name of a coefficient. This is used for Term, ContinuousTerm and FunctionTerm.

source
RegressionTables.FixedEffectCoefName — Type
struct FixedEffectCoefName <: AbstractCoefName
+end

Used to store the name of a coefficient. This is used for Term, ContinuousTerm and FunctionTerm.

source
RegressionTables.FixedEffectCoefName — Type
struct FixedEffectCoefName <: AbstractCoefName
     name::AbstractCoefName
-end

Used to store the name of a coefficient for a FixedEffectTerm. The name is the name of the fixed effect. This allows a suffix to be applied later.

source
RegressionTables.InteractedCoefName — Type
struct InteractedCoefName <: AbstractCoefName
+end

Used to store the name of a coefficient for a FixedEffectTerm. The name is the name of the fixed effect. This allows a suffix to be applied later.

source
RegressionTables.InteractedCoefName — Type
struct InteractedCoefName <: AbstractCoefName
     names::Vector
 end

Used to store the different coefficient names that makes up an InteractionTerm. The internals of the vector are typically CoefName, but can also be strings.

In a regression, each element of the vector is typically displayed as "name1 & name2 & ..." (in AsciiTables). The separator is set by interaction_combine function, and the default varies based on AbstractRenderType:

  • For AbstractAscii, it defaults to " & "
  • For AbstractLaTeX, it defaults to " $\times$ "
  • For AbstractHtml, it defaults to " &times; "

You can change the separator by running:

RegressionTables.interaction_combine(render::$RenderType) = " & "

where $RenderType is the type of the renderer you want to change. For example, to change the output in AbstractLaTeX:

RegressionTables.interaction_combine(::AbstractLaTeX) = " \& "

You can control how interaction terms are displayed more generally by changing:

Base.repr(render::AbstractRenderType, x::RegressionTables.InteractedCoefName; args...) =
-    join(RegressionTables.value.(x), RegressionTables.interaction_combine(render))

See Customization of Defaults for more details.

source
RegressionTables.InterceptCoefName — Type
struct InterceptCoefName <: AbstractCoefName end

Used as a simple indicator for the existence of an intercept term. This allows relabeling of the intercept in all cases by setting

RegressionTables.label(::InterceptCoefName) = "My Intercept"

See Customization of Defaults for more details.

source
RegressionTables.RandomEffectCoefName — Type
struct RandomEffectCoefName <: AbstractCoefName
+    join(RegressionTables.value.(x), RegressionTables.interaction_combine(render))

See Customization of Defaults for more details.

source
RegressionTables.InterceptCoefName — Type
struct InterceptCoefName <: AbstractCoefName end

Used as a simple indicator for the existence of an intercept term. This allows relabeling of the intercept in all cases by setting

RegressionTables.label(::InterceptCoefName) = "My Intercept"

See Customization of Defaults for more details.

source
RegressionTables.RandomEffectCoefName — Type
struct RandomEffectCoefName <: AbstractCoefName
     rhs::CoefName
     lhs::AbstractCoefName
     val::Float64
-end

Used to store the name and the standard deviation of a coefficient for a RandomEffectTerm from MixedModels.jl. The standard deviation is stored since that is often the useful information on the relationship between rhs and lhs.

source

Utility Functions

RegressionTables.calc_widths — Function
calc_widths(rows::Vector{DataRow{T}}) where {T<:AbstractRenderType}

Calculate the widths of each column in the table. For rows with multicolumn cells, the width of the multicolumn is divided evenly among the columns it spans.

source
RegressionTables.update_widths! — Function
update_widths!(row::DataRow{T}, new_lengths=length.(repr.(T(), row.data))) where {T}

Updates the widths of each column in the row. If lengths are provided, then it should equate to the total number of columns in the table, not the number of elements in the row.

source
RegressionTables.value_pos — Function
value_pos(nms, x::String)

Returns the position of the string x in the vector of strings or AbstractCoefName nms.

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
+end

Used to store the name and the standard deviation of a coefficient for a RandomEffectTerm from MixedModels.jl. The standard deviation is stored since that is often the useful information on the relationship between rhs and lhs.

source

Utility Functions

RegressionTables.calc_widths — Function
calc_widths(rows::Vector{DataRow{T}}) where {T<:AbstractRenderType}

Calculate the widths of each column in the table. For rows with multicolumn cells, the width of the multicolumn is divided evenly among the columns it spans.

source
RegressionTables.update_widths! — Function
update_widths!(row::DataRow{T}, new_lengths=length.(repr.(T(), row.data))) where {T}

Updates the widths of each column in the row. If lengths are provided, then it should equate to the total number of columns in the table, not the number of elements in the row.

source
RegressionTables.value_pos — Function
value_pos(nms, x::String)

Returns the position of the string x in the vector of strings or AbstractCoefName nms.

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
 
 julia> nms = ["coef1", CoefName("coef2"), InterceptCoefName(), InteractedCoefName(["coef3", "coef4"]), InteractedCoefName([CoefName("coef1"), CoefName("coef3")]), CategoricalCoefName("coef5", "10"), CategoricalCoefName("coef5", "20")];
 
@@ -137,7 +137,7 @@
 5:5
 
 julia> value_pos(nms, "coef5: 10")
-6:6
source
value_pos(nms, x::Int)

Checks that x is a valid index for the vector of strings or AbstractCoefName nms and returns a range of x:x

source
value_pos(nms, x::UnitRange)

Checks that x is a valid index for the vector of strings or AbstractCoefName nms and returns x

source
value_pos(nms, x::BitVector)

Returns a vector of indices where x is true, called by the regex version of value_pos.

source
value_pos(nms, x::Regex)

Looks within each element of the vector nms (which is either a string or an AbstractCoefName) and returns a vector of indices where the regex x is found.

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
+6:6
source
value_pos(nms, x::Int)

Checks that x is a valid index for the vector of strings or AbstractCoefName nms and returns a range of x:x

source
value_pos(nms, x::UnitRange)

Checks that x is a valid index for the vector of strings or AbstractCoefName nms and returns x

source
value_pos(nms, x::BitVector)

Returns a vector of indices where x is true, called by the regex version of value_pos.

source
value_pos(nms, x::Regex)

Looks within each element of the vector nms (which is either a string or an AbstractCoefName) and returns a vector of indices where the regex x is found.

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
 
 julia> nms = ["coef1", CoefName("coef2"), InterceptCoefName(), InteractedCoefName(["coef3", "coef4"]), InteractedCoefName([CoefName("coef1"), CoefName("coef3")]), CategoricalCoefName("coef5", "10"), CategoricalCoefName("coef5", "20")];
 
@@ -157,7 +157,7 @@
 true
 
 julia> value_pos(nms, r"coef5: 10") == [6]
-true
source
value_pos(nms, x::Nothing)

Returns an empty vector, called by the regex and string version of value_pos.

source
value_pos(nms, x::Symbol)

Expects a symbol of the form :last or :end and returns the last value of nms, both are included for consistency with the Tuple

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
+true
source
value_pos(nms, x::Nothing)

Returns an empty vector, called by the regex and string version of value_pos.

source
value_pos(nms, x::Symbol)

Expects a symbol of the form :last or :end and returns the last value of nms, both are included for consistency with the Tuple

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
 
 julia> nms = ["coef1", CoefName("coef2"), InterceptCoefName(), InteractedCoefName(["coef3", "coef4"]), InteractedCoefName([CoefName("coef1"), CoefName("coef3")]), CategoricalCoefName("coef5", "10"), CategoricalCoefName("coef5", "20")];
 
@@ -165,7 +165,7 @@
 7:7
 
 julia> value_pos(nms, :end)
-7:7
source
value_pos(nms, x::Tuple{Symbol, Int})

Expects a tuple of the form (:last, n) or (:end, n). (:last, n) returns the last n values (1:5 with (:last, 2) is 4:5), while (:end, n) returns the last index minus n (1:5 with (:end, 2) is 3).

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
+7:7
source
value_pos(nms, x::Tuple{Symbol, Int})

Expects a tuple of the form (:last, n) or (:end, n). (:last, n) returns the last n values (1:5 with (:last, 2) is 4:5), while (:end, n) returns the last index minus n (1:5 with (:end, 2) is 3).

Example

julia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos
 
 julia> nms = ["coef1", CoefName("coef2"), InterceptCoefName(), InteractedCoefName(["coef3", "coef4"]), InteractedCoefName([CoefName("coef1"), CoefName("coef3")]), CategoricalCoefName("coef5", "10"), CategoricalCoefName("coef5", "20")];
 
@@ -173,7 +173,7 @@
 6:7
 
 julia> value_pos(nms, (:end, 2))
-5:5
source
RegressionTables.combine_other_statistics — Function
combine_other_statistics(combine_other_statistics(
+5:5
source
RegressionTables.combine_other_statistics — Function
combine_other_statistics(combine_other_statistics(
     stats;
     fill_val=missing,
     print_fe_suffix=true,
@@ -181,9 +181,9 @@
     labels=Dict{String, String}(),
     transform_labels=Dict{String, String}(),
     kwargs...
-)

Takes vector of nothing or statistics and combines these into a single section. These stats should be a vector of pairs, so this function expects a vector of these vector pairs or nothing. The function will combine the pairs into a single matrix. The first matrix column is a list of unique names which are the first value of the pairs and the rest of the matrix is the last value of the pairs organized.

Arguments

  • stats is a Vector of Vector{Pair} or nothing that should be combined.
  • fill_val defaults to missing but is the default value if the statistic is not present in that regression. For example, with fixed effects this defaults to FixedEffectValue(false) if the fixed effect is not present in the regression.
  • fixedeffects is a Vector of fixed effects to include in the table. Defaults to Vector{String}(), which means all fixed effects are included. Can also be regex, integers or ranges to select which fixed effects
  • print_fe_suffix is a Bool that governs whether the fixed effects should be printed with a suffix. Defaults to true, which means the fixed effects will be printed as $X Fixed Effects. If false, the fixed effects will just be the fixed effect ($X).
  • labels is the labels from the main regtable, used to change the names of the names.
  • transform_labels is the transform_labels from the main regtable.
  • kwargs are any other kwargs passed to regtable, allowing for flexible arguments for truly custom type naming.
source
RegressionTables.combine_statistics — Function
combine_statistics(tables, stats)

Takes a set of tables (RegressionModels) and a vector of AbstractRegressionStatistic. The stats argument can also be a pair of AbstractRegressionStatistic => String, which uses the second value as the name of the statistic in the final table.

source
RegressionTables.build_nm_list — Function
build_nm_list(nms, keep)

Takes the list of strings or AbstractCoefName nms and returns a subset of nms that contains only the elements in keep. Will also reorder the elements that are kept based on the order of keep.

source
RegressionTables.reorder_nms_list — Function
reorder_nms_list(nms, order)

Reorders the vector of strings or AbstractCoefName nms according to the order provided. All elements of nms are kept.

source
RegressionTables.drop_names! — Function
drop_names!(nms, to_drop)

Drops the elements of nms that are in to_drop. Does not reorder any other elements.

source
RegressionTables.add_blank — Function
add_blank(groups::Matrix, n)
-add_blank(groups::Vector{Vector}, n)

Recursively checks whether the number of columns in groups (or the length of the vector groups) is less than n, if so, add a blank column (or element) to the left of the matrix (first element of the vector).

This is used to make sure a provided piece of data is at least n columns, fitting into the table.

source
RegressionTables.missing_vars — Function
missing_vars(table::RegressionModel, coefs::Vector)

Checks whether any of the coefficients in table are not in coefs, returns true if so, false otherwise.

source
RegressionTables.add_element! — Function
add_element!(row::DataRow, val, align_i, colwidth_i, print_underline_i, i)

Adds an element to the row and is only called when combine_equals=true. This means that if the last element in the row is the same as the provided val, then the last element is extended to include the new column.

source
add_element!(row::DataRow, val::Pair, align_i, colwidth_i, print_underline_i, i)

Adds an element to the row and is only called when combine_equals=true. Since val is a pair, is is simply added to the row as is, this allows for the creation of two multicolumn cells that have the same information but are separate.

source
RegressionTables.find_vertical_gaps — Function
find_vertical_gaps(data::Vector{DataRow{T}}) where T

Finds locations in a vector of DataRow objects where there are vertical gaps. A "vertical gap" is a place where two underlined cells are not connected. If there is a gap, then there needs to be a space between the current column and the next column. This makes it unnecessary to have a final vertical gap since the table ends there.

source
RegressionTables.extra_cell_space — Function
extra_cell_space(render::AbstractRenderType)

Used to add extra space to the cell and defaults to 0.

source

How Types are Displayed

This section describes how different types are displayed. Throughout this package, repr(T(), x) where T is a concrete type of AbstractRenderType is used to convert something to a string. This allows two things.

  1. Since it is easy to create new AbstractRenderTypes, it is possible to create customized displays for almost anyway situation
  2. Since most things in this package are types, each can be customized
Base.repr — Function
Base.repr(render::AbstractRenderType, x; args...)

Will render x as a string

source
Base.repr(render::AbstractRenderType, x::Pair; args...)

By default, will render the first element of the pair according to the render type. In cases of AbstractLatex or AbstractHtml, uses the second element to determine number of columns to span.

source
Base.repr(render::AbstractRenderType, x::Int; args...)

By default, will render the integer with commas

source
Base.repr(render::AbstractRenderType, x::Float64; digits=default_digits(render, x), commas=true, str_format=nothing, args...)

By default, will render the float with commas and the default number of digits. If str_format is specified, will use that instead of digits and commas

source
Base.repr(render::AbstractRenderType, x::Nothing; args...)

By default, will render the nothing as an empty string

source
Base.repr(render::AbstractRenderType, x::Missing; args...)

By default, will render the missing as an empty string

source
Base.repr(render::AbstractRenderType, x::AbstractString; args...)

By default, will render the string as is

source
Base.repr(render::AbstractRenderType, x::Bool; args...)

By default, will render the boolean as "Yes" or ""

source
Base.repr(render::AbstractRenderType, x::AbstractRegressionStatistic; digits=default_digits(render, x), args...)

By default, will render the statistic with commas and the default number of digits

source
Base.repr(render::AbstractRenderType, x::AbstractR2; digits=default_digits(render, x), args...)

By default, will render the same as AbstractRegressionStatistic

source
Base.repr(render::AbstractRenderType, x::AbstractUnderStatistic; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses (1.234)

source
Base.repr(render::AbstractRenderType, x::ConfInt; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses (1.234, 5.678)

source
Base.repr(render::AbstractRenderType, x::CoefValue; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses and will call estim_decorator for the decoration

source
Base.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: AbstractRegressionStatistic}

By default, will call the label function related to the type

source
Base.repr(render::AbstractRenderType, x::Type{RegressionType}; args...)

By default, will call the label function related to the RegressionType

source
Base.repr(render::AbstractRenderType, x::Tuple; args...)

By default, will render the tuple with spaces between the elements

source
Base.repr(render::AbstractRenderType, x::AbstractCoefName; args...)

By default, will render the name of the coefficient, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::FixedEffectCoefName; args...)

By default, will render the coefficient and add " Fixed Effects" as a suffix, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)

By default, will render the coefficient and add the " Clustering" function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::InteractedCoefName; args...)

By default, will render the coefficient and add the interaction_combine function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::CategoricalCoefName; args...)

By default, will render the coefficient and add the categorical_equal function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::InterceptCoefName; args...)

By default, will render the coefficient as (Intercept), also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::HasControls; args...)

By default, will render the same as Bool ("Yes" and "")

source
Base.repr(render::AbstractRenderType, x::RegressionNumbers; args...)

By default, will render the number in parentheses (e.g., "(1)", "(2)"...)

source
Base.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: HasControls}

By default, will call the label function related to the type

source
Base.repr(render::AbstractRenderType, x::RegressionType; args...)

By default, will check if the RegressionType is an instrumental variable regression and call the label function related to the type. If it is an instrumental variable, will then call label_iv, otherwise it will call the related distribution.

source
Base.repr(render::AbstractRenderType, x::D; args...) where {D <: UnivariateDistribution}

Will print the distribution as is (e.g., Probit will be "Probit"), unless another function overrides this behavior (e.g., Normal, InverseGaussian, NegativeBinomial)

source
Base.repr(render::AbstractRenderType, x::InverseGaussian; args...)

By default, will be "Inverse Gaussian"

source
Base.repr(render::AbstractRenderType, x::NegativeBinomial; args...)

By default, will be "Negative Binomial"

source
Base.repr(render::AbstractRenderType, x::Normal; args...)

By default, will call label_ols

source
Base.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)

How to render a RandomEffectCoefName and defaults to the right hand side, then the separator, then the left hand side.

source
Base.repr(render::AbstractRenderType, x::FixedEffectValue; args...)

How to render a FixedEffectValue and defaults to calling fe_value

source
Base.repr(render::AbstractRenderType, x::ClusterValue; args...)

How to render a ClusterValue and defaults to how true and false is displayed.

If wanting to show the size of the clusters, run:

RegressionTables.repr(render::AbstractRenderType, x::ClusterValue; args...) = repr(render, value(x); args...)
source
Base.repr(render::AbstractRenderType, x::RandomEffectValue; args...)

How to render a RandomEffectValue and defaults to calling another render function, dependent on the type of the value

source

New RegressionModel Types

This package is designed to be generally compatible with the RegressionModel abstraction. It has special conditions defined around four commonly used packages (FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl). It is possible to add new models to this list, either by creating an extension for this package or by creating the necessary items in an independent package.

For any new RegressionModel, there may be a need to define the following functions for the package to work correctly. Many of these will work without any issues if following the StatsModels API, and many of the others are useful for customizing how the regression result is displayed. It is also possible to redefine how RegressionTables.AbstractRegressionStatistic are displayed.

RegressionTables._formula — Function
_formula(x::RegressionModel)

Generally a passthrough for the formula function from the StatsModels package. Note tha the formula function returns the FormulaSchema.

This function is only used internally in the RegressionTables._responsename and RegressionTables._coefnames functions. Therefore, if the RegressionModel uses those two functions without using formula, this function is not necessary.

source
RegressionTables._responsename — Function
_responsename(x::RegressionModel)

Returns the name of the dependent variable in the regression model. The default of this returns a AbstractCoefName object, but it can be a String or Symbol as well.

source
RegressionTables._coefnames — Function
_coefnames(x::RegressionModel)

Returns a vector of the names of the coefficients in the regression model. The default of this returns a vector of AbstractCoefName objects, but it can be a vector of String or Symbol as well.

source
RegressionTables._coef — Function
_coef(x::RegressionModel)

Returns a vector of the coefficients in the regression model. By default, is just a passthrough for the coef function from the StatsModels package.

source
RegressionTables._stderror — Function
_stderror(x::RegressionModel)

Returns a vector of the standard errors of the coefficients in the regression model. By default, is just a passthrough for the stderror function from the StatsModels package.

source
RegressionTables._dof_residual — Function
_dof_residual(x::RegressionModel)

Returns the degrees of freedom of the residuals in the regression model. By default, is just a passthrough for the dof_residual function from the StatsModels package.

source
RegressionTables._pvalue — Function
_pvalue(x::RegressionModel)

Returns a vector of the p-values of the coefficients in the regression model.

source
RegressionTables.other_stats — Function
other_stats(rr::RegressionModel, s::Symbol)

Returns any other statistics to be displayed. This is used (if the appropriate extension is loaded) to display the fixed effects in a FixedEffectModel (or GLFixedEffectModel), clusters in those two, or Random Effects in a MixedModel. For other regressions, this returns nothing.

source
RegressionTables.default_regression_statistics — Method
default_regression_statistics(rr::RegressionModel)

Returns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.

source
RegressionTables.can_standardize — Function
can_standardize(x::RegressionModel)

Returns a boolean indicating whether the coefficients can be standardized. standardized coefficients are coefficients that are scaled by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.

This is only possible of the RegressionModel includes the model matrix or the standard deviation of the dependent variable. If the RegressionModel does not include either of these, then this function should return false.

See also RegressionTables.standardize_coef_values.

source
RegressionTables.standardize_coef_values — Function
standardize_coef_values(std_X, std_Y, val)

Standardizes the coefficients by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.

This function is only used if the RegressionTables.can_standardize function returns true.

Arguments

  • std_X::Real: The standard deviation of the independent variable.
  • std_Y::Real: The standard deviation of the dependent variable.
  • val::Real: The value to be standardized (either the coefficient or the standard error).
Note

If the standard deviation of the independent variable is 0, then the interpretation of the coefficient is how many standard deviations of the dependent variable away from 0 is the intercept. In this case, the function returns val / std_Y.

Otherwise, the function returns val * std_X / std_Y.

source
RegressionTables.RegressionType — Type
struct RegressionType{T}
+)

Takes vector of nothing or statistics and combines these into a single section. These stats should be a vector of pairs, so this function expects a vector of these vector pairs or nothing. The function will combine the pairs into a single matrix. The first matrix column is a list of unique names which are the first value of the pairs and the rest of the matrix is the last value of the pairs organized.

Arguments

  • stats is a Vector of Vector{Pair} or nothing that should be combined.
  • fill_val defaults to missing but is the default value if the statistic is not present in that regression. For example, with fixed effects this defaults to FixedEffectValue(false) if the fixed effect is not present in the regression.
  • fixedeffects is a Vector of fixed effects to include in the table. Defaults to Vector{String}(), which means all fixed effects are included. Can also be regex, integers or ranges to select which fixed effects
  • print_fe_suffix is a Bool that governs whether the fixed effects should be printed with a suffix. Defaults to true, which means the fixed effects will be printed as $X Fixed Effects. If false, the fixed effects will just be the fixed effect ($X).
  • labels is the labels from the main regtable, used to change the names of the names.
  • transform_labels is the transform_labels from the main regtable.
  • kwargs are any other kwargs passed to regtable, allowing for flexible arguments for truly custom type naming.
source
RegressionTables.combine_statistics — Function
combine_statistics(tables, stats)

Takes a set of tables (RegressionModels) and a vector of AbstractRegressionStatistic. The stats argument can also be a pair of AbstractRegressionStatistic => String, which uses the second value as the name of the statistic in the final table.

source
RegressionTables.build_nm_list — Function
build_nm_list(nms, keep)

Takes the list of strings or AbstractCoefName nms and returns a subset of nms that contains only the elements in keep. Will also reorder the elements that are kept based on the order of keep.

source
RegressionTables.reorder_nms_list — Function
reorder_nms_list(nms, order)

Reorders the vector of strings or AbstractCoefName nms according to the order provided. All elements of nms are kept.

source
RegressionTables.drop_names! — Function
drop_names!(nms, to_drop)

Drops the elements of nms that are in to_drop. Does not reorder any other elements.

source
RegressionTables.add_blank — Function
add_blank(groups::Matrix, n)
+add_blank(groups::Vector{Vector}, n)

Recursively checks whether the number of columns in groups (or the length of the vector groups) is less than n, if so, add a blank column (or element) to the left of the matrix (first element of the vector).

This is used to make sure a provided piece of data is at least n columns, fitting into the table.

source
RegressionTables.missing_vars — Function
missing_vars(table::RegressionModel, coefs::Vector)

Checks whether any of the coefficients in table are not in coefs, returns true if so, false otherwise.

source
RegressionTables.add_element! — Function
add_element!(row::DataRow, val, align_i, colwidth_i, print_underline_i, i)

Adds an element to the row and is only called when combine_equals=true. This means that if the last element in the row is the same as the provided val, then the last element is extended to include the new column.

source
add_element!(row::DataRow, val::Pair, align_i, colwidth_i, print_underline_i, i)

Adds an element to the row and is only called when combine_equals=true. Since val is a pair, is is simply added to the row as is, this allows for the creation of two multicolumn cells that have the same information but are separate.

source
RegressionTables.find_vertical_gaps — Function
find_vertical_gaps(data::Vector{DataRow{T}}) where T

Finds locations in a vector of DataRow objects where there are vertical gaps. A "vertical gap" is a place where two underlined cells are not connected. If there is a gap, then there needs to be a space between the current column and the next column. This makes it unnecessary to have a final vertical gap since the table ends there.

source
RegressionTables.extra_cell_space — Function
extra_cell_space(render::AbstractRenderType)

Used to add extra space to the cell and defaults to 0.

source

How Types are Displayed

This section describes how different types are displayed. Throughout this package, repr(T(), x) where T is a concrete type of AbstractRenderType is used to convert something to a string. This allows two things.

  1. Since it is easy to create new AbstractRenderTypes, it is possible to create customized displays for almost anyway situation
  2. Since most things in this package are types, each can be customized
Base.repr — Function
Base.repr(render::AbstractRenderType, x; args...)

Will render x as a string

source
Base.repr(render::AbstractRenderType, x::Pair; args...)

By default, will render the first element of the pair according to the render type. In cases of AbstractLatex or AbstractHtml, uses the second element to determine number of columns to span.

source
Base.repr(render::AbstractRenderType, x::Int; args...)

By default, will render the integer with commas

source
Base.repr(render::AbstractRenderType, x::Float64; digits=default_digits(render, x), commas=true, str_format=nothing, args...)

By default, will render the float with commas and the default number of digits. If str_format is specified, will use that instead of digits and commas

source
Base.repr(render::AbstractRenderType, x::Nothing; args...)

By default, will render the nothing as an empty string

source
Base.repr(render::AbstractRenderType, x::Missing; args...)

By default, will render the missing as an empty string

source
Base.repr(render::AbstractRenderType, x::AbstractString; args...)

By default, will render the string as is

source
Base.repr(render::AbstractRenderType, x::Bool; args...)

By default, will render the boolean as "Yes" or ""

source
Base.repr(render::AbstractRenderType, x::AbstractRegressionStatistic; digits=default_digits(render, x), args...)

By default, will render the statistic with commas and the default number of digits

source
Base.repr(render::AbstractRenderType, x::AbstractR2; digits=default_digits(render, x), args...)

By default, will render the same as AbstractRegressionStatistic

source
Base.repr(render::AbstractRenderType, x::AbstractUnderStatistic; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses (1.234)

source
Base.repr(render::AbstractRenderType, x::ConfInt; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses (1.234, 5.678)

source
Base.repr(render::AbstractRenderType, x::CoefValue; digits=default_digits(render, x), args...)

By default, will render with the default number of digits and surrounded by parentheses and will call estim_decorator for the decoration

source
Base.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: AbstractRegressionStatistic}

By default, will call the label function related to the type

source
Base.repr(render::AbstractRenderType, x::Type{RegressionType}; args...)

By default, will call the label function related to the RegressionType

source
Base.repr(render::AbstractRenderType, x::Tuple; args...)

By default, will render the tuple with spaces between the elements

source
Base.repr(render::AbstractRenderType, x::AbstractCoefName; args...)

By default, will render the name of the coefficient, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::FixedEffectCoefName; args...)

By default, will render the coefficient and add " Fixed Effects" as a suffix, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)

By default, will render the coefficient and add the " Clustering" function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::InteractedCoefName; args...)

By default, will render the coefficient and add the interaction_combine function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::CategoricalCoefName; args...)

By default, will render the coefficient and add the categorical_equal function as a separator, also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::InterceptCoefName; args...)

By default, will render the coefficient as (Intercept), also see Regression Statistics

source
Base.repr(render::AbstractRenderType, x::HasControls; args...)

By default, will render the same as Bool ("Yes" and "")

source
Base.repr(render::AbstractRenderType, x::RegressionNumbers; args...)

By default, will render the number in parentheses (e.g., "(1)", "(2)"...)

source
Base.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: HasControls}

By default, will call the label function related to the type

source
Base.repr(render::AbstractRenderType, x::RegressionType; args...)

By default, will check if the RegressionType is an instrumental variable regression and call the label function related to the type. If it is an instrumental variable, will then call label_iv, otherwise it will call the related distribution.

source
Base.repr(render::AbstractRenderType, x::D; args...) where {D <: UnivariateDistribution}

Will print the distribution as is (e.g., Probit will be "Probit"), unless another function overrides this behavior (e.g., Normal, InverseGaussian, NegativeBinomial)

source
Base.repr(render::AbstractRenderType, x::InverseGaussian; args...)

By default, will be "Inverse Gaussian"

source
Base.repr(render::AbstractRenderType, x::NegativeBinomial; args...)

By default, will be "Negative Binomial"

source
Base.repr(render::AbstractRenderType, x::Normal; args...)

By default, will call label_ols

source
Base.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)

How to render a RandomEffectCoefName and defaults to the right hand side, then the separator, then the left hand side.

source
Base.repr(render::AbstractRenderType, x::FixedEffectValue; args...)

How to render a FixedEffectValue and defaults to calling fe_value

source
Base.repr(render::AbstractRenderType, x::ClusterValue; args...)

How to render a ClusterValue and defaults to how true and false is displayed.

If wanting to show the size of the clusters, run:

RegressionTables.repr(render::AbstractRenderType, x::ClusterValue; args...) = repr(render, value(x); args...)
source
Base.repr(render::AbstractRenderType, x::RandomEffectValue; args...)

How to render a RandomEffectValue and defaults to calling another render function, dependent on the type of the value

source

New RegressionModel Types

This package is designed to be generally compatible with the RegressionModel abstraction. It has special conditions defined around four commonly used packages (FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl). It is possible to add new models to this list, either by creating an extension for this package or by creating the necessary items in an independent package.

For any new RegressionModel, there may be a need to define the following functions for the package to work correctly. Many of these will work without any issues if following the StatsModels API, and many of the others are useful for customizing how the regression result is displayed. It is also possible to redefine how RegressionTables.AbstractRegressionStatistic are displayed.

RegressionTables._formula — Function
_formula(x::RegressionModel)

Generally a passthrough for the formula function from the StatsModels package. Note tha the formula function returns the FormulaSchema.

This function is only used internally in the RegressionTables._responsename and RegressionTables._coefnames functions. Therefore, if the RegressionModel uses those two functions without using formula, this function is not necessary.

source
RegressionTables._responsename — Function
_responsename(x::RegressionModel)

Returns the name of the dependent variable in the regression model. The default of this returns a AbstractCoefName object, but it can be a String or Symbol as well.

source
RegressionTables._coefnames — Function
_coefnames(x::RegressionModel)

Returns a vector of the names of the coefficients in the regression model. The default of this returns a vector of AbstractCoefName objects, but it can be a vector of String or Symbol as well.

source
RegressionTables._coef — Function
_coef(x::RegressionModel)

Returns a vector of the coefficients in the regression model. By default, is just a passthrough for the coef function from the StatsModels package.

source
RegressionTables._stderror — Function
_stderror(x::RegressionModel)

Returns a vector of the standard errors of the coefficients in the regression model. By default, is just a passthrough for the stderror function from the StatsModels package.

source
RegressionTables._dof_residual — Function
_dof_residual(x::RegressionModel)

Returns the degrees of freedom of the residuals in the regression model. By default, is just a passthrough for the dof_residual function from the StatsModels package.

source
RegressionTables._pvalue — Function
_pvalue(x::RegressionModel)

Returns a vector of the p-values of the coefficients in the regression model.

source
RegressionTables.other_stats — Function
other_stats(rr::RegressionModel, s::Symbol)

Returns any other statistics to be displayed. This is used (if the appropriate extension is loaded) to display the fixed effects in a FixedEffectModel (or GLFixedEffectModel), clusters in those two, or Random Effects in a MixedModel. For other regressions, this returns nothing.

source
RegressionTables.default_regression_statistics — Method
default_regression_statistics(rr::RegressionModel)

Returns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.

source
RegressionTables.can_standardize — Function
can_standardize(x::RegressionModel)

Returns a boolean indicating whether the coefficients can be standardized. standardized coefficients are coefficients that are scaled by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.

This is only possible of the RegressionModel includes the model matrix or the standard deviation of the dependent variable. If the RegressionModel does not include either of these, then this function should return false.

See also RegressionTables.standardize_coef_values.

source
RegressionTables.standardize_coef_values — Function
standardize_coef_values(std_X, std_Y, val)

Standardizes the coefficients by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.

This function is only used if the RegressionTables.can_standardize function returns true.

Arguments

  • std_X::Real: The standard deviation of the independent variable.
  • std_Y::Real: The standard deviation of the dependent variable.
  • val::Real: The value to be standardized (either the coefficient or the standard error).
Note

If the standard deviation of the independent variable is 0, then the interpretation of the coefficient is how many standard deviations of the dependent variable away from 0 is the intercept. In this case, the function returns val / std_Y.

Otherwise, the function returns val * std_X / std_Y.

source
RegressionTables.RegressionType — Type
struct RegressionType{T}
     val::T
     is_iv::Bool
 end

The type of the regression. val should be a distribution from the Distributions.jl package. is_iv indicates whether the regression is an instrumental variable regression. The default label for the regression type is "Estimator". The labels for individual regression types (e.g., "OLS", "Poisson") can be set by running:

RegressionTables.label_ols(render::AbstractRenderType) = $name
-RegressionTables.label_iv(render::AbstractRenderType) = $name

Or for individual distributions by running:

Base.repr(render::AbstractRenderType, x::$Distribution; args...) = $Name
source
+RegressionTables.label_iv(render::AbstractRenderType) = $name

Or for individual distributions by running:

Base.repr(render::AbstractRenderType, x::$Distribution; args...) = $Name
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rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark 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.content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content 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kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content 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html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark 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.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark 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.button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark 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.button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 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img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 img,html.theme--documenter-dark .image.is-3by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,html.theme--documenter-dark .image.is-2by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 img,html.theme--documenter-dark .image.is-2by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,html.theme--documenter-dark .image.is-3by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--documenter-dark .image.is-3by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--documenter-dark .image.is-9by16 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--documenter-dark .image.is-9by16 .has-ratio,html.theme--documenter-dark #documenter 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.docs-logo>img.is-5by4{padding-top:80%}html.theme--documenter-dark .image.is-4by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--documenter-dark .image.is-3by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--documenter-dark .image.is-5by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--documenter-dark .image.is-16by9,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--documenter-dark .image.is-2by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--documenter-dark .image.is-3by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3{padding-top:300%}html.theme--documenter-dark .image.is-16x16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16x16{height:16px;width:16px}html.theme--documenter-dark .image.is-24x24,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-24x24{height:24px;width:24px}html.theme--documenter-dark .image.is-32x32,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-32x32{height:32px;width:32px}html.theme--documenter-dark .image.is-48x48,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-48x48{height:48px;width:48px}html.theme--documenter-dark .image.is-64x64,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-64x64{height:64px;width:64px}html.theme--documenter-dark .image.is-96x96,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-96x96{height:96px;width:96px}html.theme--documenter-dark .image.is-128x128,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-128x128{height:128px;width:128px}html.theme--documenter-dark .notification{background-color:#282f2f;border-radius:.4em;position:relative;padding:1.25rem 2.5rem 1.25rem 1.5rem}html.theme--documenter-dark .notification a:not(.button):not(.dropdown-item){color:currentColor;text-decoration:underline}html.theme--documenter-dark .notification strong{color:currentColor}html.theme--documenter-dark .notification code,html.theme--documenter-dark .notification pre{background:#fff}html.theme--documenter-dark .notification pre code{background:transparent}html.theme--documenter-dark .notification>.delete{right:.5rem;position:absolute;top:0.5rem}html.theme--documenter-dark .notification .title,html.theme--documenter-dark .notification .subtitle,html.theme--documenter-dark .notification .content{color:currentColor}html.theme--documenter-dark .notification.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .notification.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark 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.progress::-ms-fill{background-color:#dbdee0;border:none}html.theme--documenter-dark .progress.is-white::-webkit-progress-value{background-color:#fff}html.theme--documenter-dark .progress.is-white::-moz-progress-bar{background-color:#fff}html.theme--documenter-dark .progress.is-white::-ms-fill{background-color:#fff}html.theme--documenter-dark .progress.is-white:indeterminate{background-image:linear-gradient(to right, #fff 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-black::-webkit-progress-value{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-moz-progress-bar{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-ms-fill{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black:indeterminate{background-image:linear-gradient(to right, #0a0a0a 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-light::-webkit-progress-value{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-moz-progress-bar{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-ms-fill{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light:indeterminate{background-image:linear-gradient(to right, #ecf0f1 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-dark::-webkit-progress-value,html.theme--documenter-dark .content kbd.progress::-webkit-progress-value{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-moz-progress-bar,html.theme--documenter-dark .content kbd.progress::-moz-progress-bar{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-ms-fill,html.theme--documenter-dark .content kbd.progress::-ms-fill{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark:indeterminate,html.theme--documenter-dark .content kbd.progress:indeterminate{background-image:linear-gradient(to right, #282f2f 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-primary::-webkit-progress-value,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-webkit-progress-value{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-moz-progress-bar,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-moz-progress-bar{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-ms-fill,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-ms-fill{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary:indeterminate,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink:indeterminate{background-image:linear-gradient(to right, #375a7f 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-link::-webkit-progress-value{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-moz-progress-bar{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-ms-fill{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link:indeterminate{background-image:linear-gradient(to right, #1abc9c 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-info::-webkit-progress-value{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-moz-progress-bar{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-ms-fill{background-color:#024c7d}html.theme--documenter-dark .progress.is-info:indeterminate{background-image:linear-gradient(to right, #024c7d 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-success::-webkit-progress-value{background-color:#008438}html.theme--documenter-dark .progress.is-success::-moz-progress-bar{background-color:#008438}html.theme--documenter-dark .progress.is-success::-ms-fill{background-color:#008438}html.theme--documenter-dark .progress.is-success:indeterminate{background-image:linear-gradient(to right, #008438 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-warning::-webkit-progress-value{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-moz-progress-bar{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-ms-fill{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning:indeterminate{background-image:linear-gradient(to right, #ad8100 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-danger::-webkit-progress-value{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-moz-progress-bar{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-ms-fill{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger:indeterminate{background-image:linear-gradient(to right, #9e1b0d 30%, #343c3d 30%)}html.theme--documenter-dark 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.table{background-color:#343c3d;color:#fff}html.theme--documenter-dark .table td,html.theme--documenter-dark .table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .table td.is-white,html.theme--documenter-dark .table th.is-white{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .table td.is-black,html.theme--documenter-dark .table th.is-black{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .table td.is-light,html.theme--documenter-dark .table th.is-light{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .table td.is-dark,html.theme--documenter-dark .table th.is-dark{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .table td.is-primary,html.theme--documenter-dark .table th.is-primary{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-link,html.theme--documenter-dark .table th.is-link{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .table td.is-info,html.theme--documenter-dark .table th.is-info{background-color:#024c7d;border-color:#024c7d;color:#fff}html.theme--documenter-dark .table td.is-success,html.theme--documenter-dark .table th.is-success{background-color:#008438;border-color:#008438;color:#fff}html.theme--documenter-dark .table td.is-warning,html.theme--documenter-dark .table th.is-warning{background-color:#ad8100;border-color:#ad8100;color:#fff}html.theme--documenter-dark .table td.is-danger,html.theme--documenter-dark .table th.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .table td.is-narrow,html.theme--documenter-dark .table th.is-narrow{white-space:nowrap;width:1%}html.theme--documenter-dark .table td.is-selected,html.theme--documenter-dark .table th.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-selected a,html.theme--documenter-dark .table td.is-selected strong,html.theme--documenter-dark .table th.is-selected a,html.theme--documenter-dark .table th.is-selected strong{color:currentColor}html.theme--documenter-dark .table td.is-vcentered,html.theme--documenter-dark .table th.is-vcentered{vertical-align:middle}html.theme--documenter-dark .table th{color:#f2f2f2}html.theme--documenter-dark .table th:not([align]){text-align:left}html.theme--documenter-dark .table tr.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table tr.is-selected a,html.theme--documenter-dark .table tr.is-selected strong{color:currentColor}html.theme--documenter-dark .table tr.is-selected td,html.theme--documenter-dark .table tr.is-selected th{border-color:#fff;color:currentColor}html.theme--documenter-dark .table thead{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table thead td,html.theme--documenter-dark .table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .table tfoot{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tfoot td,html.theme--documenter-dark .table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .table tbody{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tbody tr:last-child td,html.theme--documenter-dark .table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .table.is-bordered td,html.theme--documenter-dark .table.is-bordered th{border-width:1px}html.theme--documenter-dark .table.is-bordered tr:last-child td,html.theme--documenter-dark .table.is-bordered tr:last-child th{border-bottom-width:1px}html.theme--documenter-dark .table.is-fullwidth{width:100%}html.theme--documenter-dark .table.is-hoverable tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover:nth-child(even){background-color:#2d3435}html.theme--documenter-dark .table.is-narrow td,html.theme--documenter-dark .table.is-narrow th{padding:0.25em 0.5em}html.theme--documenter-dark .table.is-striped tbody tr:not(.is-selected):nth-child(even){background-color:#282f2f}html.theme--documenter-dark .table-container{-webkit-overflow-scrolling:touch;overflow:auto;overflow-y:hidden;max-width:100%}html.theme--documenter-dark .tags{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .tags .tag,html.theme--documenter-dark .tags .content kbd,html.theme--documenter-dark .content .tags kbd,html.theme--documenter-dark .tags .docstring>section>a.docs-sourcelink{margin-bottom:0.5rem}html.theme--documenter-dark .tags .tag:not(:last-child),html.theme--documenter-dark .tags .content 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.button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) 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rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content 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kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus:not(:active),html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--documenter-dark .button.is-primary.is-focused:not(:active),html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark 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.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark 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.button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fff7de;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fff3d1;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark 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.button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 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Theme: Default Description: Original highlight.js style Author: (c) Ivan Sagalaev diff --git a/dev/customization/index.html b/dev/customization/index.html index a15124f..c454d8d 100644 --- a/dev/customization/index.html +++ b/dev/customization/index.html @@ -16,7 +16,7 @@ "1.2346" julia> repr(LatexTable(), x) -"1.2346"source
default_digits(render::AbstractRenderType, x::AbstractUnderStatistic)

Default for under statistics (TStat, StdError), defaults to the general setting of 3 digits

Examples

julia> RegressionTables.default_digits(::RegressionTables.AbstractAscii, x::RegressionTables.AbstractUnderStatistic) = 4;
+"1.2346"
source
default_digits(render::AbstractRenderType, x::AbstractUnderStatistic)

Default for under statistics (TStat, StdError), defaults to the general setting of 3 digits

Examples

julia> RegressionTables.default_digits(::RegressionTables.AbstractAscii, x::RegressionTables.AbstractUnderStatistic) = 4;
 
 julia> x = StdError(1.234567);
 
@@ -25,12 +25,12 @@
 
 julia> repr(LatexTable(), x) # unchanged since the default_digits was only changed for Ascii
 "(1.235)"
-
source
default_digits(render::AbstractRenderType, x::.CoefValue)

Default for CoefValue, defaults to the general setting of 3 digits

Examples

julia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.CoefValue) = 2
+
source
default_digits(render::AbstractRenderType, x::.CoefValue)

Default for CoefValue, defaults to the general setting of 3 digits

Examples

julia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.CoefValue) = 2
 
 julia> x = RegressionTables.CoefValue(1.234567, 1); # 1 is for the p value
 
 julia> repr(HtmlTable(), x)
-"1.23"
source
default_digits(render::AbstractRenderType, x)

The default for for all other values not otherwise specified, defaults to 3 digits

Examples

julia> RegressionTables.default_digits(::AbstractRenderType, x) = 4
+"1.23"
source
default_digits(render::AbstractRenderType, x)

The default for for all other values not otherwise specified, defaults to 3 digits

Examples

julia> RegressionTables.default_digits(::AbstractRenderType, x) = 4
 
 julia> x = 1.234567;
 
@@ -40,19 +40,19 @@
 "1.2346"
 
 julia> repr(LatexTable(), y) # Also changes since the default_digits for other types default to this value
-"(1.2346)"
source

Default Keyword Arguments

RegressionTables.default_section_order — Function
default_section_order(render::AbstractRenderType)

Default section order for the table, defaults to [:groups, :depvar, :number_regressions, :break, :coef, :break, :fe, :break, :randomeffects, :break, :clusters, :break, :regtype, :break, :controls, :break, :stats, :extralines]

:break is a special keyword that adds a line break between sections (e.g. between \midrule in Latex)

source
RegressionTables.default_align — Function
default_align(render::AbstractRenderType)

Defaults to :r for all render types, possible options are :r, :l, :c This affects each part of the table after the header and the leftmost column always has the :l alignment

source
RegressionTables.default_print_fe — Function
default_print_fe(render::AbstractRenderType, rrs)

Defaults to true, but the section will not be printed if there are not fixed effects. If false the fixed effects are not printed

source
RegressionTables.default_groups — Function
default_groups(render::AbstractRenderType, rrs)

Defaults to nothing, groups are printed above the dependent variable Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.

source
RegressionTables.default_extralines — Function
default_extralines(render::AbstractRenderType, rrs)

Defaults to nothing, extra lines are printed at the end of the table. Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.

source
RegressionTables.default_order — Function
default_order(render::AbstractRenderType, rrs)

Defaults to Vector{String}(), which means the order is not changed. Also see Keep Drop and Order Arguments for more information.

In settings where the primary variables of interest are static throughout the tests, it can help to set default_order to a regex that includes that variable. For example, if the primary variables are interactions, then

RegressionTables.default_order(::AbstractRenderType, rrs) = [r" & "]

will prioritize the interactions in the table.

source
RegressionTables.default_labels — Function
default_labels(render::AbstractRenderType, rrs)

Defaults to Dict{String, String}(), which means no coefficients are changed. If you have a master dictionary of variables to change, it can help to set default_labels to that dictionary. It is also possible to set default_labels for each table type, allowing for labels to escape special characters in Latex.

Examples

RegressionTables.default_labels(render::AbstractRenderType, rrs) = Dict("first" => "New First", "second" => "X > Y")
-RegressionTables.default_labels(render::RegressionTables.AbstractLatex, rrs) = Dict("first" => "New First", "second" => "X $>$ Y")
source
RegressionTables.default_transform_labels — Function
default_transform_labels(render::AbstractRenderType, rrs) = Dict{String, String}()
-default_transform_labels(render::AbstractLatex, rrs) = :latex

transform_labels apply a replace function to the coefficients, dependent variables and fixed effects. The default for AbstractLatex is used to escape special characters in Latex.

source
RegressionTables.default_stat_below — Function
default_stat_below(render::AbstractRenderType)

Defaults to true, which means the standard error (or t-stat) is printed below the coefficient. If false, the standard error is printed to the right of the coefficient (in the same column)

source
RegressionTables.default_file — Function
default_file(render::AbstractRenderType, renderSettings::Tuple{<:AbstractRenderType, String}, rrs)

Defaults to nothing, which means no file is saved.

source
RegressionTables.default_standardize_coef — Function
default_standardize_coef(render::AbstractRenderType, rrs)

Defaults to false. Standardizing the coefficient divides the coefficient by its standard deviation and multiplies it by the standard deviation of the dependent variable. It is only possible for models that store the matrix, such as those in GLM.jl and MixedModels.jl. If it is not possible, the coefficients will not change.

source
RegressionTables.default_print_estimator — Function
default_print_estimator(render::AbstractRenderType, rrs)

Defaults to true if more than one type of estimator is used. For example, if all regressions are "OLS", then this section will default to false, while if one regression is "OLS" and another is "IV", then this section will default to true.

source
RegressionTables.default_regression_statistics — Function
default_regression_statistics(rr::RegressionModel)

Returns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.

source
default_regression_statistics(render::AbstractRenderType, rrs)

Defaults to a union of the default_regression_statistics for each regression. For example, an "OLS" regression (with no fixed effects) will default to including [Nobs, R2], and a Probit regression will include [Nobs, PseudoR2], so the default will be [Nobs, R2, PseudoR2].

source
RegressionTables.default_use_relabeled_values — Function
default_use_relabeled_values(render::AbstractRenderType, rrs) = true

Defaults to true, which means the keep, drop and order arguments will use the relabeled values instead of the original values.

source

Other Defaults

While the user can adjust almost any part of this package (see How Types are Displayed), here are the remaining major defaults that are settable.

RegressionTables.default_breaks — Function
default_breaks(render::AbstractRenderType)

The default cutoffs for the number of "*" (or other value set by default_symbol). The default is [0.001, 0.01, 0.05], corresponding to a p-value of 0.1%, 1%, and 5%.

source

Default Keyword Arguments

RegressionTables.default_section_order — Function
default_section_order(render::AbstractRenderType)

Default section order for the table, defaults to [:groups, :depvar, :number_regressions, :break, :coef, :break, :fe, :break, :randomeffects, :break, :clusters, :break, :regtype, :break, :controls, :break, :stats, :extralines]

:break is a special keyword that adds a line break between sections (e.g. between \midrule in Latex)

source
RegressionTables.default_align — Function
default_align(render::AbstractRenderType)

Defaults to :r for all render types, possible options are :r, :l, :c This affects each part of the table after the header and the leftmost column always has the :l alignment

source
RegressionTables.default_print_fe — Function
default_print_fe(render::AbstractRenderType, rrs)

Defaults to true, but the section will not be printed if there are not fixed effects. If false the fixed effects are not printed

source
RegressionTables.default_groups — Function
default_groups(render::AbstractRenderType, rrs)

Defaults to nothing, groups are printed above the dependent variable Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.

source
RegressionTables.default_extralines — Function
default_extralines(render::AbstractRenderType, rrs)

Defaults to nothing, extra lines are printed at the end of the table. Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.

source
RegressionTables.default_order — Function
default_order(render::AbstractRenderType, rrs)

Defaults to Vector{String}(), which means the order is not changed. Also see Keep Drop and Order Arguments for more information.

In settings where the primary variables of interest are static throughout the tests, it can help to set default_order to a regex that includes that variable. For example, if the primary variables are interactions, then

RegressionTables.default_order(::AbstractRenderType, rrs) = [r" & "]

will prioritize the interactions in the table.

source
RegressionTables.default_labels — Function
default_labels(render::AbstractRenderType, rrs)

Defaults to Dict{String, String}(), which means no coefficients are changed. If you have a master dictionary of variables to change, it can help to set default_labels to that dictionary. It is also possible to set default_labels for each table type, allowing for labels to escape special characters in Latex.

Examples

RegressionTables.default_labels(render::AbstractRenderType, rrs) = Dict("first" => "New First", "second" => "X > Y")
+RegressionTables.default_labels(render::RegressionTables.AbstractLatex, rrs) = Dict("first" => "New First", "second" => "X $>$ Y")
source
RegressionTables.default_transform_labels — Function
default_transform_labels(render::AbstractRenderType, rrs) = Dict{String, String}()
+default_transform_labels(render::AbstractLatex, rrs) = :latex

transform_labels apply a replace function to the coefficients, dependent variables and fixed effects. The default for AbstractLatex is used to escape special characters in Latex.

source
RegressionTables.default_stat_below — Function
default_stat_below(render::AbstractRenderType)

Defaults to true, which means the standard error (or t-stat) is printed below the coefficient. If false, the standard error is printed to the right of the coefficient (in the same column)

source
RegressionTables.default_file — Function
default_file(render::AbstractRenderType, renderSettings::Tuple{<:AbstractRenderType, String}, rrs)

Defaults to nothing, which means no file is saved.

source
RegressionTables.default_standardize_coef — Function
default_standardize_coef(render::AbstractRenderType, rrs)

Defaults to false. Standardizing the coefficient divides the coefficient by its standard deviation and multiplies it by the standard deviation of the dependent variable. It is only possible for models that store the matrix, such as those in GLM.jl and MixedModels.jl. If it is not possible, the coefficients will not change.

source
RegressionTables.default_print_estimator — Function
default_print_estimator(render::AbstractRenderType, rrs)

Defaults to true if more than one type of estimator is used. For example, if all regressions are "OLS", then this section will default to false, while if one regression is "OLS" and another is "IV", then this section will default to true.

source
RegressionTables.default_regression_statistics — Function
default_regression_statistics(rr::RegressionModel)

Returns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.

source
default_regression_statistics(render::AbstractRenderType, rrs)

Defaults to a union of the default_regression_statistics for each regression. For example, an "OLS" regression (with no fixed effects) will default to including [Nobs, R2], and a Probit regression will include [Nobs, PseudoR2], so the default will be [Nobs, R2, PseudoR2].

source
RegressionTables.default_use_relabeled_values — Function
default_use_relabeled_values(render::AbstractRenderType, rrs) = true

Defaults to true, which means the keep, drop and order arguments will use the relabeled values instead of the original values.

source

Other Defaults

While the user can adjust almost any part of this package (see How Types are Displayed), here are the remaining major defaults that are settable.

RegressionTables.default_breaks — Function
default_breaks(render::AbstractRenderType)

The default cutoffs for the number of "*" (or other value set by default_symbol). The default is [0.001, 0.01, 0.05], corresponding to a p-value of 0.1%, 1%, and 5%.

source
RegressionTables.interaction_combine — Function
interaction_combine(render::AbstractRenderType) = " & "
 interaction_combine(render::AbstractLatex) = " \$\times\$ "
-interaction_combine(render::AbstractHtml) = " &times; "

Used to separate pieces of InteractedCoefName and defaults to:

  • " & " in the general case
  • "$ \times $" in Latex
  • " &times; " in HTML

Change this by rerunning the function with the desired default, for example:

RegressionTables.interaction_combine(render::AbstractLatex) = " \& "
source
RegressionTables.estim_decorator — Function
estim_decorator(render::AbstractRenderType, s, pval; breaks=default_breaks(render), sym=default_symbol(render))

Decorates a value with a symbol based on p-value. In many journals, the symbol is a * and the p-value has three cutoffs, either 0.001, 0.01, and 0.05 or 0.01, 0.05, and 0.10.

It is possible to wrap the symbols in an additional element, for example, in Latex it is common to wrap the symbol as a superscript. To do so in all tables, run

RegressionTables.wrapper(::AbstractLatex, deco) = "\$^{$deco}\$"

It is also possible to change the cutoffs or symbols. To change the cutoffs, run

RegressionTables.default_breaks(::AbstractLatex) = [0.01, 0.05, 0.10] # make sure these are in order

And to change the cutoffs run

RegressionTables.default_symbol(::AbstractLatex) = "x" # or whatever you want
source
RegressionTables.below_decoration — Function
below_decoration(render::AbstractRenderType, s)

Used to decorate a string below the main string and defaults to "($s)". Change this by running:

RegressionTables.below_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.number_regressions_decoration — Function
number_regressions_decoration(render::AbstractRenderType, s)

Used to decorate the regression number (e.g., "(1)") and defaults to "($s)". Change this by running:

RegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.fe_suffix — Function
fe_suffix(render::AbstractRenderType)

Used to add a suffix to the fixed effects and defaults to " Fixed Effects". Change this by running:

RegressionTables.fe_suffix(render::AbstractRenderType) = " Fixed Effects"
source
RegressionTables.wrapper — Function
wrapper(render::AbstractRenderType, s)

Used to wrap a string, particularly for estim_decorator and defaults to s (i.e., no wrapper). For example, to wrap the stars in Latex, run:

RegressionTables.wrapper(::AbstractLatex, deco) = "$^{$deco}$"
source
RegressionTables.fe_value — Function
fe_value(render::AbstractRenderType, v)

Determines how to render a yes/no value for fixed effects, defaults to "Yes" and "". Can be changed by running:

RegressionTables.fe_value(render::AbstractRenderType, v) = v ? "Yes" : "No"
source
RegressionTables.cluster_suffix — Function
cluster_suffix(render::AbstractRenderType)

Used to add a suffix to the clustering and defaults to " Clustering". Change this by running:

RegressionTables.cluster_suffix(render::AbstractRenderType) = " Clustering"
source

Labels

Labels are customizable by running the function that defines them. This makes it possible to change the labels once and then not worry about them on subsequent tables. To change a label, run:

RegressionTables.label(render::AbstractRenderType, ::Type{Nobs}) = "Obs."

Labels use the Julia type system, so it is possible to create different labels depending on the table type. This is done by default for cases such as Nobs and R2, where the defaults for Latex and HTML are different. In such cases, it is necessary to set the label for all types that are used:

RegressionTables.label(render::AbstractLatex, ::Type{Nobs}) = "Obs."
+interaction_combine(render::AbstractHtml) = " &times; "

Used to separate pieces of InteractedCoefName and defaults to:

Change this by rerunning the function with the desired default, for example:

RegressionTables.interaction_combine(render::AbstractLatex) = " \& "
source
RegressionTables.estim_decorator — Function
estim_decorator(render::AbstractRenderType, s, pval; breaks=default_breaks(render), sym=default_symbol(render))

Decorates a value with a symbol based on p-value. In many journals, the symbol is a * and the p-value has three cutoffs, either 0.001, 0.01, and 0.05 or 0.01, 0.05, and 0.10.

It is possible to wrap the symbols in an additional element, for example, in Latex it is common to wrap the symbol as a superscript. To do so in all tables, run

RegressionTables.wrapper(::AbstractLatex, deco) = "\$^{$deco}\$"

It is also possible to change the cutoffs or symbols. To change the cutoffs, run

RegressionTables.default_breaks(::AbstractLatex) = [0.01, 0.05, 0.10] # make sure these are in order

And to change the cutoffs run

RegressionTables.default_symbol(::AbstractLatex) = "x" # or whatever you want
source
RegressionTables.below_decoration — Function
below_decoration(render::AbstractRenderType, s)

Used to decorate a string below the main string and defaults to "($s)". Change this by running:

RegressionTables.below_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.number_regressions_decoration — Function
number_regressions_decoration(render::AbstractRenderType, s)

Used to decorate the regression number (e.g., "(1)") and defaults to "($s)". Change this by running:

RegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.fe_suffix — Function
fe_suffix(render::AbstractRenderType)

Used to add a suffix to the fixed effects and defaults to " Fixed Effects". Change this by running:

RegressionTables.fe_suffix(render::AbstractRenderType) = " Fixed Effects"
source
RegressionTables.wrapper — Function
wrapper(render::AbstractRenderType, s)

Used to wrap a string, particularly for estim_decorator and defaults to s (i.e., no wrapper). For example, to wrap the stars in Latex, run:

RegressionTables.wrapper(::AbstractLatex, deco) = "$^{$deco}$"
source
RegressionTables.fe_value — Function
fe_value(render::AbstractRenderType, v)

Determines how to render a yes/no value for fixed effects, defaults to "Yes" and "". Can be changed by running:

RegressionTables.fe_value(render::AbstractRenderType, v) = v ? "Yes" : "No"
source
RegressionTables.cluster_suffix — Function
cluster_suffix(render::AbstractRenderType)

Used to add a suffix to the clustering and defaults to " Clustering". Change this by running:

RegressionTables.cluster_suffix(render::AbstractRenderType) = " Clustering"
source

Labels

Labels are customizable by running the function that defines them. This makes it possible to change the labels once and then not worry about them on subsequent tables. To change a label, run:

RegressionTables.label(render::AbstractRenderType, ::Type{Nobs}) = "Obs."

Labels use the Julia type system, so it is possible to create different labels depending on the table type. This is done by default for cases such as Nobs and R2, where the defaults for Latex and HTML are different. In such cases, it is necessary to set the label for all types that are used:

RegressionTables.label(render::AbstractLatex, ::Type{Nobs}) = "Obs."
 RegressionTables.label(render::AbstractHtml, ::Type{Nobs}) = "Obs."

Some labels (notably the R2 group), call another label function. For example:

label(render::AbstractRenderType, ::Type{AdjR2}) = "Adjusted " * label(render, R2)

Ths means that the label for AdjR2 relies on the label for R2. This also means that changing the label for R2 will change the labels for all other R2 types.

RegressionTables.label — Function
label(render::AbstractRenderType, x::Type{Nobs}) = "N"
 label(render::AbstractLatex, x::Type{Nobs}) = "\$N\$"
-label(render::AbstractHtml, x::Type{Nobs}) = "<i>N</i>"
source
label(render::AbstractRenderType, x::Type{R2}) = "R2"
+label(render::AbstractHtml, x::Type{Nobs}) = "<i>N</i>"
source
label(render::AbstractRenderType, x::Type{R2}) = "R2"
 label(render::AbstractLatex, x::Type{R2}) = "\$R^2\$"
-label(render::AbstractHtml, x::Type{R2}) = "<i>R</i><sup>2</sup>"
source
label(render::AbstractRenderType, x::Type{R2McFadden}) = "Pseudo " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2CoxSnell}) = "Cox-Snell " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2Nagelkerke}) = "Nagelkerke " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2Deviance}) = "Deviance " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{AdjR2}) = "Adjusted " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{AdjR2McFadden}) = "Pseudo " * label(render, AdjR2)
source
label(render::AbstractRenderType, x::Type{AdjR2Deviance}) = "Deviance " * label(render, AdjR2)
source
label(render::AbstractRenderType, x::Type{DOF}) = "Degrees of Freedom"
source
label(render::AbstractRenderType, x::Type{LogLikelihood}) = "Log Likelihood"
source
label(render::AbstractRenderType, x::Type{AIC}) = "AIC"
source
label(render::AbstractRenderType, x::Type{AICC}) = "AICC"
source
label(render::AbstractRenderType, x::Type{BIC}) = "BIC"
source
label(render::AbstractRenderType, x::Type{FStat}) = "F"
+label(render::AbstractHtml, x::Type{R2}) = "<i>R</i><sup>2</sup>"
source
label(render::AbstractRenderType, x::Type{R2McFadden}) = "Pseudo " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2CoxSnell}) = "Cox-Snell " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2Nagelkerke}) = "Nagelkerke " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{R2Deviance}) = "Deviance " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{AdjR2}) = "Adjusted " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{AdjR2McFadden}) = "Pseudo " * label(render, AdjR2)
source
label(render::AbstractRenderType, x::Type{AdjR2Deviance}) = "Deviance " * label(render, AdjR2)
source
label(render::AbstractRenderType, x::Type{DOF}) = "Degrees of Freedom"
source
label(render::AbstractRenderType, x::Type{LogLikelihood}) = "Log Likelihood"
source
label(render::AbstractRenderType, x::Type{AIC}) = "AIC"
source
label(render::AbstractRenderType, x::Type{AICC}) = "AICC"
source
label(render::AbstractRenderType, x::Type{BIC}) = "BIC"
source
label(render::AbstractRenderType, x::Type{FStat}) = "F"
 label(render::AbstractLatex, x::Type{FStat}) = "\$F\$"
-label(render::AbstractHtml, x::Type{FStat}) = "<i>F</i>"
source
label(render::AbstractRenderType, x::Type{FStatPValue}) = label(render, FStat) * "-test " * label_p(render) * " value"
source
label(render::AbstractRenderType, x::Type{FStatIV}) = "First-stage " * label(render, FStat) * " statistic"
source
label(render::AbstractRenderType, x::Type{FStatIVPValue}) = "First-stage " * label_p(render) * " value"
source
label(render::AbstractRenderType, x::Type{R2Within}) = "Within " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{RegressionType}) = "Estimator"
source
label(render::AbstractRenderType, x::Type{HasControls}) = "Controls"
source
label(render::AbstractRenderType, x::Type{RegressionNumbers}) = ""
source
RegressionTables.label_p — Function
label_p(render::AbstractRenderType) = "p"
+label(render::AbstractHtml, x::Type{FStat}) = "<i>F</i>"
source
label(render::AbstractRenderType, x::Type{FStatPValue}) = label(render, FStat) * "-test " * label_p(render) * " value"
source
label(render::AbstractRenderType, x::Type{FStatIV}) = "First-stage " * label(render, FStat) * " statistic"
source
label(render::AbstractRenderType, x::Type{FStatIVPValue}) = "First-stage " * label_p(render) * " value"
source
label(render::AbstractRenderType, x::Type{R2Within}) = "Within " * label(render, R2)
source
label(render::AbstractRenderType, x::Type{RegressionType}) = "Estimator"
source
label(render::AbstractRenderType, x::Type{HasControls}) = "Controls"
source
label(render::AbstractRenderType, x::Type{RegressionNumbers}) = ""
source
RegressionTables.label_p — Function
label_p(render::AbstractRenderType) = "p"
 label_p(render::AbstractLatex) = "\$p\$"
-label_p(render::AbstractHtml) = "<i>p</i>"
source
RegressionTables.label_distribution — Function
label_distribution(render::AbstractRenderType, d::D) where {D <: UnivariateDistribution} = string(Base.typename(D).wrapper)
+label_p(render::AbstractHtml) = "<i>p</i>"
source
RegressionTables.label_distribution — Function
label_distribution(render::AbstractRenderType, d::D) where {D <: UnivariateDistribution} = string(Base.typename(D).wrapper)
 label_distribution(render::AbstractRenderType, d::NegativeBinomial) = "Negative Binomial"
-label_distribution(render::AbstractRenderType, d::InverseGaussian) = "Inverse Gaussian"

How to label non-linear regressions and defaults to the name of the distribution. For example, Probit will be "Probit". Two exceptions are given for NegativeBinomial and InverseGaussian, which are "Negative Binomial" and "Inverse Gaussian", respectively. This can be changed for a specific distribution by running:

RegressionTables.label_distribution(render::AbstractRenderType, d::Poisson) = "Poisson"
source
+label_distribution(render::AbstractRenderType, d::InverseGaussian) = "Inverse Gaussian"

How to label non-linear regressions and defaults to the name of the distribution. For example, Probit will be "Probit". Two exceptions are given for NegativeBinomial and InverseGaussian, which are "Negative Binomial" and "Inverse Gaussian", respectively. This can be changed for a specific distribution by running:

RegressionTables.label_distribution(render::AbstractRenderType, d::Poisson) = "Poisson"
source diff --git a/dev/examples/index.html b/dev/examples/index.html index 431323c..71d2383 100644 --- a/dev/examples/index.html +++ b/dev/examples/index.html @@ -1251,4 +1251,4 @@ [_R_$""^2$] , [0.014], [], [], [Pseudo _R_$""^2$] , [0.006], [0.347], [0.297], table.hline(), -) +) diff --git a/dev/function_reference/index.html b/dev/function_reference/index.html index 5fcf09e..cd5ccd2 100644 --- a/dev/function_reference/index.html +++ b/dev/function_reference/index.html @@ -1,2 +1,2 @@ -Function and Type Reference · RegressionTables.jl
GitHub

Function Reference

+Function and Type Reference · RegressionTables.jl
GitHub

Function Reference

diff --git a/dev/index.html b/dev/index.html index 89d8117..519d027 100644 --- a/dev/index.html +++ b/dev/index.html @@ -133,4 +133,4 @@ Pseudo R2 0.006 0.006 0.104 --------------------------------------------- -

Printing of StatsBase.RegressionModels (e.g., MixedModels.jl and GLFixedEffectModels.jl) generally works but are less well tested; please file as issue if you encounter problems printing them.

Main regtable Function

RegressionTables.regtable — Function

Produces a publication-quality regression table, similar to Stata's esttab and R's stargazer.

Arguments

  • rr::FixedEffectModel... are the FixedEffectModels from FixedEffectModels.jl that should be printed. Only required argument.
  • keep is a Vector of regressor names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all regressors will be shown. The strings should be the relabeld names of the coefficients.
  • drop is a Vector of regressor names (Strings), integers, ranges or regex that should not be shown. Defaults to an empty vector, in which case no regressors will be dropped. The strings should be the relabeld names of the coefficients.
  • order is a Vector of regressor names (Strings), integers, ranges or regex that should be shown in that order. Defaults to an empty vector, in which case the order of regressors will be unchanged. Other regressors are still shown (assuming drop is empty). The strings should be the relabeld names of the coefficients.
  • fixedeffects is a Vector of FE names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all FE's will be shown.
  • align is a Symbol from the set [:l,:c,:r] indicating the alignment of results columns (default :r right-aligned). Currently works only with ASCII and LaTeX output.
  • header_align is a Symbol from the set [:l,:c,:r] indicating the alignment of the header row (default :c centered). Currently works only with ASCII and LaTeX output.
  • labels is a Dict that contains displayed labels for variables (Strings) and other text in the table. If no label for a variable is found, it default to variable names. See documentation for special values.
  • estimformat is a String that describes the format of the estimate.
  • digits is an Int that describes the precision to be shown in the estimate. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).
  • statisticformat is a String that describes the format of the number below the estimate (se/t).
  • digits_stats is an Int that describes the precision to be shown in the statistics. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).
  • below_statistic is a type that describes a statistic that should be shown below each point estimate. Recognized values are nothing, StdError, TStat, and ConfInt. nothing suppresses the line. Defaults to StdError.
  • regression_statistics is a Vector of types that describe statistics to be shown at the bottom of the table. Built in types are Recognized symbols are Nobs, R2, PseudoR2, R2CoxSnell, R2Nagelkerke, R2Deviance, AdjR2, AdjPseudoR2, AdjR2Deviance, DOF, LogLikelihood, AIC, AICC, BIC, FStat, FStatPValue, FStatIV, FStatIVPValue, R2Within. Defaults vary based on regression inputs (simple linear model is [Nobs, R2]).
  • extralines is a Vector or a Vector{<:AbsractVector} that will be added to the end of the table. A single vector will be its own row, a vector of vectors will each be a row. Defaults to nothing.
  • number_regressions is a Bool that governs whether regressions should be numbered. Defaults to true.
  • groups is a Vector, Vector{<:AbstractVector} or Matrix of labels used to group regressions. This can be useful if results are shown for different data sets or sample restrictions.
  • print_fe_section is a Bool that governs whether a section on fixed effects should be shown. Defaults to true.
  • print_estimator_section is a Bool that governs whether to print a section on which estimator (OLS/IV/Binomial/Poisson...) is used. Defaults to true if more than one value is displayed.
  • standardize_coef is a Bool that governs whether the table should show standardized coefficients. Note that this only works with TableRegressionModels, and that only coefficient estimates and the below_statistic are being standardized (i.e. the R^2 etc still pertain to the non-standardized regression).
  • render::AbstractRenderType is a AbstractRenderType type that governs how the table should be rendered. Standard supported types are ASCII (via AsciiTable()) and LaTeX (via LatexTable()). Defaults to AsciiTable().
  • file is a String that governs whether the table should be saved to a file. Defaults to nothing.
  • transform_labels is a Dict or one of the Symbols :ampersand, :underscore, :underscore2space, :latex
  • confint_level is a Float64 that governs the confidence level for the confidence interval. Defaults to 0.95.

Details

A typical use is to pass a number of FixedEffectModels to the function, along with how it should be rendered (with render argument):

regtable(regressionResult1, regressionResult2; render = AsciiTable())

Pass a string to the file argument to create or overwrite a file. For example, using LaTeX output,

regtable(regressionResult1, regressionResult2; render = LatexTable(), file="myoutfile.tex")

See the full argument list for details.

source
+

Printing of StatsBase.RegressionModels (e.g., MixedModels.jl and GLFixedEffectModels.jl) generally works but are less well tested; please file as issue if you encounter problems printing them.

Main regtable Function

RegressionTables.regtable — Function

Produces a publication-quality regression table, similar to Stata's esttab and R's stargazer.

Arguments

  • rr::FixedEffectModel... are the FixedEffectModels from FixedEffectModels.jl that should be printed. Only required argument.
  • keep is a Vector of regressor names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all regressors will be shown. The strings should be the relabeld names of the coefficients.
  • drop is a Vector of regressor names (Strings), integers, ranges or regex that should not be shown. Defaults to an empty vector, in which case no regressors will be dropped. The strings should be the relabeld names of the coefficients.
  • order is a Vector of regressor names (Strings), integers, ranges or regex that should be shown in that order. Defaults to an empty vector, in which case the order of regressors will be unchanged. Other regressors are still shown (assuming drop is empty). The strings should be the relabeld names of the coefficients.
  • fixedeffects is a Vector of FE names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all FE's will be shown.
  • align is a Symbol from the set [:l,:c,:r] indicating the alignment of results columns (default :r right-aligned). Currently works only with ASCII and LaTeX output.
  • header_align is a Symbol from the set [:l,:c,:r] indicating the alignment of the header row (default :c centered). Currently works only with ASCII and LaTeX output.
  • labels is a Dict that contains displayed labels for variables (Strings) and other text in the table. If no label for a variable is found, it default to variable names. See documentation for special values.
  • estimformat is a String that describes the format of the estimate.
  • digits is an Int that describes the precision to be shown in the estimate. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).
  • statisticformat is a String that describes the format of the number below the estimate (se/t).
  • digits_stats is an Int that describes the precision to be shown in the statistics. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).
  • below_statistic is a type that describes a statistic that should be shown below each point estimate. Recognized values are nothing, StdError, TStat, and ConfInt. nothing suppresses the line. Defaults to StdError.
  • regression_statistics is a Vector of types that describe statistics to be shown at the bottom of the table. Built in recognized types are Nobs, R2, PseudoR2, R2CoxSnell, R2Nagelkerke, R2Deviance, AdjR2, AdjPseudoR2, AdjR2Deviance, DOF, LogLikelihood, AIC, AICC, BIC, FStat, FStatPValue, FStatIV, FStatIVPValue, R2Within. Defaults vary based on regression inputs (simple linear model is [Nobs, R2]).
  • extralines is a Vector or a Vector{<:AbsractVector} that will be added to the end of the table. A single vector will be its own row, a vector of vectors will each be a row. Defaults to nothing.
  • number_regressions is a Bool that governs whether regressions should be numbered. Defaults to true.
  • groups is a Vector, Vector{<:AbstractVector} or Matrix of labels used to group regressions. This can be useful if results are shown for different data sets or sample restrictions.
  • print_fe_section is a Bool that governs whether a section on fixed effects should be shown. Defaults to true.
  • print_estimator_section is a Bool that governs whether to print a section on which estimator (OLS/IV/Binomial/Poisson...) is used. Defaults to true if more than one value is displayed.
  • standardize_coef is a Bool that governs whether the table should show standardized coefficients. Note that this only works with TableRegressionModels, and that only coefficient estimates and the below_statistic are being standardized (i.e. the R^2 etc still pertain to the non-standardized regression).
  • render::AbstractRenderType is a AbstractRenderType type that governs how the table should be rendered. Standard supported types are ASCII (via AsciiTable()) and LaTeX (via LatexTable()). Defaults to AsciiTable().
  • file is a String that governs whether the table should be saved to a file. Defaults to nothing.
  • transform_labels is a Dict or one of the Symbols :ampersand, :underscore, :underscore2space, :latex
  • confint_level is a Float64 that governs the confidence level for the confidence interval. Defaults to 0.95.

Details

A typical use is to pass a number of FixedEffectModels to the function, along with how it should be rendered (with render argument):

regtable(regressionResult1, regressionResult2; render = AsciiTable())

Pass a string to the file argument to create or overwrite a file. For example, using LaTeX output,

regtable(regressionResult1, regressionResult2; render = LatexTable(), file="myoutfile.tex")

See the full argument list for details.

source
diff --git a/dev/keep_drop_order/index.html b/dev/keep_drop_order/index.html index 9508c0c..64b888f 100644 --- a/dev/keep_drop_order/index.html +++ b/dev/keep_drop_order/index.html @@ -317,4 +317,4 @@ N 150 150 150 150 R2 0.726 0.863 0.870 0.635 Within-R2 0.281 0.642 0.659 0.391 ---------------------------------------------------------------------- +--------------------------------------------------------------------- diff --git a/dev/regression_statistics/index.html b/dev/regression_statistics/index.html index 35184e3..649065d 100644 --- a/dev/regression_statistics/index.html +++ b/dev/regression_statistics/index.html @@ -7,28 +7,28 @@ catch YMean(nothing) end -RegressionTable.label(render::AbstractRenderType, x::Type{YMean}) = "Mean of Y"source

Defined Types

RegressionTables.AIC — Type

AIC is the Akaike Information Criterion of the regression. Labels default to "AIC" for all tables.

source
RegressionTables.AICC — Type

AICC is the Corrected Akaike Information Criterion of the regression. Labels default to "AICC" for all tables.

source
RegressionTables.AbstractR2 — Type
abstract type AbstractR2 <: AbstractRegressionStatistic end

Parent type for all $R^2$ statistics. This is available to change the formatting of all $R^2$ statistics. For example, if the desired display for $R^2$ is in the percentage term, run:

Base.repr(render::AbstractRenderType, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * "%"
+RegressionTable.label(render::AbstractRenderType, x::Type{YMean}) = "Mean of Y"
source

Defined Types

RegressionTables.AIC — Type

AIC is the Akaike Information Criterion of the regression. Labels default to "AIC" for all tables.

source
RegressionTables.AICC — Type

AICC is the Corrected Akaike Information Criterion of the regression. Labels default to "AICC" for all tables.

source
RegressionTables.AbstractR2 — Type
abstract type AbstractR2 <: AbstractRegressionStatistic end

Parent type for all $R^2$ statistics. This is available to change the formatting of all $R^2$ statistics. For example, if the desired display for $R^2$ is in the percentage term, run:

Base.repr(render::AbstractRenderType, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * "%"
 # add second definition since Latex needs % escaped
-Base.repr(render::AbstractRenderType::RegressionTables.AbstractLatex, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * "\%"
source
RegressionTables.AdjR2 — Type

AdjR2 is the Adjusted $R^2$ of the regression. Labels default to:

  • "Adjusted R2" for AbstractAscii
  • "Adjusted $R^2$" for AbstractLatex
  • "Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.AdjR2Deviance — Type

AdjR2Deviance is the Deviance Adjusted $R^2$ of the regression. Labels default to:

  • "Deviance Adjusted R2" for AbstractAscii
  • "Deviance Adjusted $R^2$" for AbstractLatex
  • "Deviance Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.AdjR2McFadden — Type

AdjR2McFadden is the McFadden Adjusted $R^2$ of the regression (often referred to as the Pseudo Adjusted $R^2$). Labels default to:

  • "Pseudo Adjusted R2" for AbstractAscii
  • "Pseudo Adjusted $R^2$" for AbstractLatex
  • "Pseudo Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.BIC — Type

BIC is the Bayesian Information Criterion of the regression. Labels default to "BIC" for all tables.

source
RegressionTables.DOF — Type

DOF is the remaining degrees of freedom in the regression. Labels default to "Degrees of Freedom" for all tables.

source
RegressionTables.FStat — Type

FStat is the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "F" for AbstractAscii
  • "$F$" for AbstractLatex
  • "<i>F</i>" for AbstractHtml
Note

the FStat label is used in other labels, so changing it will change those labels as well.

source
RegressionTables.FStatIV — Type

FStatIV is the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "First-stage F statistic" for AbstractAscii
  • "First-stage $F$ statistic" for AbstractLatex
  • "First-stage <i>F</i> statistic" for AbstractHtml
source
RegressionTables.FStatIVPValue — Type

FStatIVPValue is the p-value of the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "First-stage p value" for AbstractAscii
  • "First-stage $p$ value" for AbstractLatex
  • "First-stage <i>p</i> value" for AbstractHtml
source
RegressionTables.FStatPValue — Type

FStatPValue is the p-value of the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "F p value" for AbstractAscii
  • "$F$ $p$ value" for AbstractLatex
  • "<i>F</i> <i>p</i> value" for AbstractHtml
source
RegressionTables.Nobs — Type

Nobs is the number of observations in the regression. Labels default to:

  • "N" for AbstractAscii
  • "$N$" for AbstractLatex
  • "<i>N</i>" for AbstractHtml
source
RegressionTables.R2 — Type

R2 is the $R^2$ of the regression. Labels default to:

  • "R2" for AbstractAscii
  • "$R^2$" for AbstractLatex
  • "<i>R</i><sup>2</sup>" for AbstractHtml
Note

The label for R2 is used in other related statistics. So changing the label for R2 will change the label for other $R^2$ statistics as well.

source
RegressionTables.R2CoxSnell — Type

R2CoxSnell is the Cox-Snell $R^2$ of the regression. Labels default to:

  • "Cox-Snell R2" for AbstractAscii
  • "Cox-Snell $R^2$" for AbstractLatex
  • "Cox-Snell <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Deviance — Type

R2Deviance is the Deviance $R^2$ of the regression. Labels default to:

  • "Deviance R2" for AbstractAscii
  • "Deviance $R^2$" for AbstractLatex
  • "Deviance <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2McFadden — Type

R2McFadden is the McFadden $R^2$ of the regression (often referred to as the Pseudo-$R^2$). Labels default to:

  • "Pseudo R2" for AbstractAscii
  • "Pseudo $R^2$" for AbstractLatex
  • "Pseudo <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Nagelkerke — Type

R2Nagelkerke is the Nagelkerke $R^2$ of the regression. Labels default to:

  • "Nagelkerke R2" for AbstractAscii
  • "Nagelkerke $R^2$" for AbstractLatex
  • "Nagelkerke <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Within — Type

R2Within is the within R-squared of a fixed effects regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "Within R2" for AbstractAscii
  • "Within $R^2$" for AbstractLatex
  • "Within <i>R</i><sup>2</sup>" for AbstractHtml
source

Below or Under Statistic

RegressionTables.AbstractUnderStatistic — Type
abstract type AbstractUnderStatistic end

The abstract type for statistics that are below or next to the coefficients (e.g., standard errors, t-statistics, confidence intervals, etc.). The default available values are:

New values can be added by subtyping AbstractUnderStatistic and defining the struct and a constructor. The constructer should accept the standard error, the coefficient, and the degrees of freedom.

source
RegressionTables.ConfInt — Type
struct ConfInt <: AbstractUnderStatistic
+Base.repr(render::AbstractRenderType::RegressionTables.AbstractLatex, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * "\%"
source
RegressionTables.AdjR2 — Type

AdjR2 is the Adjusted $R^2$ of the regression. Labels default to:

  • "Adjusted R2" for AbstractAscii
  • "Adjusted $R^2$" for AbstractLatex
  • "Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.AdjR2Deviance — Type

AdjR2Deviance is the Deviance Adjusted $R^2$ of the regression. Labels default to:

  • "Deviance Adjusted R2" for AbstractAscii
  • "Deviance Adjusted $R^2$" for AbstractLatex
  • "Deviance Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.AdjR2McFadden — Type

AdjR2McFadden is the McFadden Adjusted $R^2$ of the regression (often referred to as the Pseudo Adjusted $R^2$). Labels default to:

  • "Pseudo Adjusted R2" for AbstractAscii
  • "Pseudo Adjusted $R^2$" for AbstractLatex
  • "Pseudo Adjusted <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.BIC — Type

BIC is the Bayesian Information Criterion of the regression. Labels default to "BIC" for all tables.

source
RegressionTables.DOF — Type

DOF is the remaining degrees of freedom in the regression. Labels default to "Degrees of Freedom" for all tables.

source
RegressionTables.FStat — Type

FStat is the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "F" for AbstractAscii
  • "$F$" for AbstractLatex
  • "<i>F</i>" for AbstractHtml
Note

the FStat label is used in other labels, so changing it will change those labels as well.

source
RegressionTables.FStatIV — Type

FStatIV is the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "First-stage F statistic" for AbstractAscii
  • "First-stage $F$ statistic" for AbstractLatex
  • "First-stage <i>F</i> statistic" for AbstractHtml
source
RegressionTables.FStatIVPValue — Type

FStatIVPValue is the p-value of the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "First-stage p value" for AbstractAscii
  • "First-stage $p$ value" for AbstractLatex
  • "First-stage <i>p</i> value" for AbstractHtml
source
RegressionTables.FStatPValue — Type

FStatPValue is the p-value of the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "F p value" for AbstractAscii
  • "$F$ $p$ value" for AbstractLatex
  • "<i>F</i> <i>p</i> value" for AbstractHtml
source
RegressionTables.Nobs — Type

Nobs is the number of observations in the regression. Labels default to:

  • "N" for AbstractAscii
  • "$N$" for AbstractLatex
  • "<i>N</i>" for AbstractHtml
source
RegressionTables.R2 — Type

R2 is the $R^2$ of the regression. Labels default to:

  • "R2" for AbstractAscii
  • "$R^2$" for AbstractLatex
  • "<i>R</i><sup>2</sup>" for AbstractHtml
Note

The label for R2 is used in other related statistics. So changing the label for R2 will change the label for other $R^2$ statistics as well.

source
RegressionTables.R2CoxSnell — Type

R2CoxSnell is the Cox-Snell $R^2$ of the regression. Labels default to:

  • "Cox-Snell R2" for AbstractAscii
  • "Cox-Snell $R^2$" for AbstractLatex
  • "Cox-Snell <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Deviance — Type

R2Deviance is the Deviance $R^2$ of the regression. Labels default to:

  • "Deviance R2" for AbstractAscii
  • "Deviance $R^2$" for AbstractLatex
  • "Deviance <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2McFadden — Type

R2McFadden is the McFadden $R^2$ of the regression (often referred to as the Pseudo-$R^2$). Labels default to:

  • "Pseudo R2" for AbstractAscii
  • "Pseudo $R^2$" for AbstractLatex
  • "Pseudo <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Nagelkerke — Type

R2Nagelkerke is the Nagelkerke $R^2$ of the regression. Labels default to:

  • "Nagelkerke R2" for AbstractAscii
  • "Nagelkerke $R^2$" for AbstractLatex
  • "Nagelkerke <i>R</i><sup>2</sup>" for AbstractHtml
source
RegressionTables.R2Within — Type

R2Within is the within R-squared of a fixed effects regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:

  • "Within R2" for AbstractAscii
  • "Within $R^2$" for AbstractLatex
  • "Within <i>R</i><sup>2</sup>" for AbstractHtml
source

Below or Under Statistic

RegressionTables.AbstractUnderStatistic — Type
abstract type AbstractUnderStatistic end

The abstract type for statistics that are below or next to the coefficients (e.g., standard errors, t-statistics, confidence intervals, etc.). The default available values are:

New values can be added by subtyping AbstractUnderStatistic and defining the struct and a constructor. The constructer should accept the standard error, the coefficient, and the degrees of freedom.

source
RegressionTables.ConfInt — Type
struct ConfInt <: AbstractUnderStatistic
     val::Tuple{Float64, Float64}
 end
-ConfInt(rr::RegressionModel, k::Int; level=0.95, standardize=false, vargs...)

The confidence interval of a coefficient. The default confidence level is 95% (can be changed by setting RegressionTable.default_confint_level(render::AbstractRenderType, rr) = 0.90 or similar).

source
RegressionTables.StdError — Type
struct StdError <: AbstractUnderStatistic
+ConfInt(rr::RegressionModel, k::Int; level=0.95, standardize=false, vargs...)

The confidence interval of a coefficient. The default confidence level is 95% (can be changed by setting RegressionTable.default_confint_level(render::AbstractRenderType, rr) = 0.90 or similar).

source
RegressionTables.StdError — Type
struct StdError <: AbstractUnderStatistic
     val::Float64
 end
-StdError(rr::RegressionModel, k::Int; standardize=false, vargs...)

The standard error of a coefficient.

source
RegressionTables.TStat — Type
struct TStat <: AbstractUnderStatistic
+StdError(rr::RegressionModel, k::Int; standardize=false, vargs...)

The standard error of a coefficient.

source
RegressionTables.TStat — Type
struct TStat <: AbstractUnderStatistic
     val::Float64
 end
-TStat(rr::RegressionModel, k::Int; vargs...)

The t-statistic of a coefficient.

source

Other Statistics

Other Statistics

RegressionTables.HasControls — Type
struct HasControls
     val::Bool
-end

Indicates whether the regression has coefficients left out of the table. HasControls is used as a label, which defaults to "Controls". This can be changed by setting

RegressionTables.label(render::AbstractRenderType, x::Type{RegressionTables.HasControls}) = $name
source
RegressionTables.RegressionNumbers — Type
struct RegressionNumbers
+end

Indicates whether the regression has coefficients left out of the table. HasControls is used as a label, which defaults to "Controls". This can be changed by setting

RegressionTables.label(render::AbstractRenderType, x::Type{RegressionTables.HasControls}) = $name
source
RegressionTables.RegressionNumbers — Type
struct RegressionNumbers
     val::Int
-end

Used to define which column number the regression is in. Primarily, this is used to control how these values are displayed. The default displays these as ($i), which can be set by running

RegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.FixedEffectValue — Type
struct FixedEffectValue
+end

Used to define which column number the regression is in. Primarily, this is used to control how these values are displayed. The default displays these as ($i), which can be set by running

RegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = "($s)"
source
RegressionTables.FixedEffectValue — Type
struct FixedEffectValue
     val::Bool
-end

A simple store of true/false for whether a fixed effect is used in the regression, used to determine how to display the value. The default is "Yes" and "", which can be changed by setting fe_value.

source
RegressionTables.RandomEffectValue — Type
struct RandomEffectValue
+end

A simple store of true/false for whether a fixed effect is used in the regression, used to determine how to display the value. The default is "Yes" and "", which can be changed by setting fe_value.

source
RegressionTables.RandomEffectValue — Type
struct RandomEffectValue
     val::Real
-end

A simple sotre of the random effect value, by default equal to the standard deviation of the random effect. Typically will then be displayed the same as other Float64 values.

source
RegressionTables.ClusterValue — Type
struct ClusterValue
+end

A simple sotre of the random effect value, by default equal to the standard deviation of the random effect. Typically will then be displayed the same as other Float64 values.

source
RegressionTables.ClusterValue — Type
struct ClusterValue
     val::Int
-end

A simple store of the number of clusters used in the regression. Typically will be displayed the same as other Bool values (e.g., "Yes" or "").

source
+end

A simple store of the number of clusters used in the regression. Typically will be displayed the same as other Bool values (e.g., "Yes" or "").

source diff --git a/dev/search_index.js b/dev/search_index.js index f69a5be..a3c4121 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"customization/#Customization-of-Defaults","page":"Customization and Defaults","title":"Customization of Defaults","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Pages=[\"customization.md\"]","category":"page"},{"location":"customization/#Principles","page":"Customization and Defaults","title":"Principles","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Within most publications, the tables look similar. This package tries to provide easy access to almost any setting so you can \"set and forget\" while producing tables that you need. For example, suppose you are using Latex and want to use the tabular* environment instead of the default tabular. Thanks to the Julia type system, this is possible, simply run","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.tablestart(::RegressionTables.AbstractLatex, align) = \"\\\\begin{tabular*}{\\\\linewidth}{$(align[1])@{\\\\extracolsep{\\\\fill}}$(align[2:end])}\"\nRegressionTables.tableend(::RegressionTables.AbstractLatex) = \"\\\\end{tabular*}\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"These two lines change all AbstractLatex tables.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"The Julia type system also allows customization for more individualized tables. For example, you might include some descriptive tables in a paper, but it might make sense to use different rounding for descriptive data (such as making sure floats that are stored as integers are displayed as integers or rounding for large numbers). This needs to happen without changing the rounding in most tables. To do so, you can create a new type and set the display option for that type:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"struct LatexDescriptiveTable <: RegressionTables.AbstractLatex end\nfunction Base.repr(::LatexDescriptiveTable, x::Float64; args...)\n if isinteger(x)\n format(Int(x); commas=true)\n else\n precision = x > 1000 ? 1 : 3\n format(x; commas=true, precision)\n end\nend","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Now, when creating a descriptive table (see RegressionTable for an example), pass render=LatexDescriptiveTable() to use your formatting function.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"The rest of this page goes through the defaults, showing what they are and what you might change each to.","category":"page"},{"location":"customization/#Rounding-Digits","page":"Customization and Defaults","title":"Rounding Digits","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_digits","category":"page"},{"location":"customization/#RegressionTables.default_digits","page":"Customization and Defaults","title":"RegressionTables.default_digits","text":"default_digits(render::AbstractRenderType, x::AbstractRegressionStatistic)\n\nDefault for regression statistics (R2, AIC), defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.AbstractRegressionStatistic) = 4;\n\njulia> x = R2(1.234567);\n\njulia> repr(AsciiTable(), x)\n\"1.2346\"\n\njulia> repr(LatexTable(), x)\n\"1.2346\"\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x::AbstractUnderStatistic)\n\nDefault for under statistics (TStat, StdError), defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::RegressionTables.AbstractAscii, x::RegressionTables.AbstractUnderStatistic) = 4;\n\njulia> x = StdError(1.234567);\n\njulia> repr(AsciiTable(), x)\n\"(1.2346)\"\n\njulia> repr(LatexTable(), x) # unchanged since the default_digits was only changed for Ascii\n\"(1.235)\"\n\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x::.CoefValue)\n\nDefault for CoefValue, defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.CoefValue) = 2\n\njulia> x = RegressionTables.CoefValue(1.234567, 1); # 1 is for the p value\n\njulia> repr(HtmlTable(), x)\n\"1.23\"\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x)\n\nThe default for for all other values not otherwise specified, defaults to 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x) = 4\n\njulia> x = 1.234567;\n\njulia> y = TStat(1.234567);\n\njulia> repr(LatexTable(), x)\n\"1.2346\"\n\njulia> repr(LatexTable(), y) # Also changes since the default_digits for other types default to this value\n\"(1.2346)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#Default-Keyword-Arguments","page":"Customization and Defaults","title":"Default Keyword Arguments","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_section_order\nRegressionTables.default_align\nRegressionTables.default_header_align\nRegressionTables.default_depvar\nRegressionTables.default_number_regressions\nRegressionTables.default_print_fe\nRegressionTables.default_groups\nRegressionTables.default_extralines\nRegressionTables.default_keep\nRegressionTables.default_drop\nRegressionTables.default_order\nRegressionTables.default_fixedeffects\nRegressionTables.default_labels\nRegressionTables.default_transform_labels\nRegressionTables.default_below_statistic\nRegressionTables.default_stat_below\nRegressionTables.default_render\nRegressionTables.default_file\nRegressionTables.default_print_fe_suffix\nRegressionTables.default_print_control_indicator\nRegressionTables.default_standardize_coef\nRegressionTables.default_print_estimator\nRegressionTables.default_regression_statistics\nRegressionTables.default_print_randomeffects\nRegressionTables.default_print_clusters\nRegressionTables.default_use_relabeled_values\nRegressionTables.default_confint_level","category":"page"},{"location":"customization/#RegressionTables.default_section_order","page":"Customization and Defaults","title":"RegressionTables.default_section_order","text":"default_section_order(render::AbstractRenderType)\n\nDefault section order for the table, defaults to [:groups, :depvar, :number_regressions, :break, :coef, :break, :fe, :break, :randomeffects, :break, :clusters, :break, :regtype, :break, :controls, :break, :stats, :extralines]\n\n:break is a special keyword that adds a line break between sections (e.g. between \\midrule in Latex)\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_align","page":"Customization and Defaults","title":"RegressionTables.default_align","text":"default_align(render::AbstractRenderType)\n\nDefaults to :r for all render types, possible options are :r, :l, :c This affects each part of the table after the header and the leftmost column always has the :l alignment\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_header_align","page":"Customization and Defaults","title":"RegressionTables.default_header_align","text":"default_header_align(render::AbstractRenderType)\n\nDefaults to :c for all render types, possible options are :r, :l, :c This affects the header (all sections up before :coef, see default_section_order) of each part of the table\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_depvar","page":"Customization and Defaults","title":"RegressionTables.default_depvar","text":"default_depvar(render::AbstractRenderType)\n\nDefaults to true for all render types, if false the dependent variable (\"Y\") is not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_number_regressions","page":"Customization and Defaults","title":"RegressionTables.default_number_regressions","text":"default_number_regressions(render::AbstractRenderType, rrs)\n\nDefaults to true if there is more than one regression, if false the regression number is not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_fe","page":"Customization and Defaults","title":"RegressionTables.default_print_fe","text":"default_print_fe(render::AbstractRenderType, rrs)\n\nDefaults to true, but the section will not be printed if there are not fixed effects. If false the fixed effects are not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_groups","page":"Customization and Defaults","title":"RegressionTables.default_groups","text":"default_groups(render::AbstractRenderType, rrs)\n\nDefaults to nothing, groups are printed above the dependent variable Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_extralines","page":"Customization and Defaults","title":"RegressionTables.default_extralines","text":"default_extralines(render::AbstractRenderType, rrs)\n\nDefaults to nothing, extra lines are printed at the end of the table. Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_keep","page":"Customization and Defaults","title":"RegressionTables.default_keep","text":"default_keep(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means all variables are printed. Also see Keep Drop and Order Arguments for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_drop","page":"Customization and Defaults","title":"RegressionTables.default_drop","text":"default_drop(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means no variables are dropped. Also see Keep Drop and Order Arguments for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_order","page":"Customization and Defaults","title":"RegressionTables.default_order","text":"default_order(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means the order is not changed. Also see Keep Drop and Order Arguments for more information.\n\nIn settings where the primary variables of interest are static throughout the tests, it can help to set default_order to a regex that includes that variable. For example, if the primary variables are interactions, then\n\nRegressionTables.default_order(::AbstractRenderType, rrs) = [r\" & \"]\n\nwill prioritize the interactions in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_fixedeffects","page":"Customization and Defaults","title":"RegressionTables.default_fixedeffects","text":"default_fixedeffects(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means any fixed effects available are printed.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_labels","page":"Customization and Defaults","title":"RegressionTables.default_labels","text":"default_labels(render::AbstractRenderType, rrs)\n\nDefaults to Dict{String, String}(), which means no coefficients are changed. If you have a master dictionary of variables to change, it can help to set default_labels to that dictionary. It is also possible to set default_labels for each table type, allowing for labels to escape special characters in Latex.\n\nExamples\n\nRegressionTables.default_labels(render::AbstractRenderType, rrs) = Dict(\"first\" => \"New First\", \"second\" => \"X > Y\")\nRegressionTables.default_labels(render::RegressionTables.AbstractLatex, rrs) = Dict(\"first\" => \"New First\", \"second\" => \"X $>$ Y\")\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_transform_labels","page":"Customization and Defaults","title":"RegressionTables.default_transform_labels","text":"default_transform_labels(render::AbstractRenderType, rrs) = Dict{String, String}()\ndefault_transform_labels(render::AbstractLatex, rrs) = :latex\n\ntransform_labels apply a replace function to the coefficients, dependent variables and fixed effects. The default for AbstractLatex is used to escape special characters in Latex.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_below_statistic","page":"Customization and Defaults","title":"RegressionTables.default_below_statistic","text":"default_below_statistic(render::AbstractRenderType)\n\nDefaults to StdError, which means the standard error is printed below the coefficient. See AbstractUnderStatistic for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_stat_below","page":"Customization and Defaults","title":"RegressionTables.default_stat_below","text":"default_stat_below(render::AbstractRenderType)\n\nDefaults to true, which means the standard error (or t-stat) is printed below the coefficient. If false, the standard error is printed to the right of the coefficient (in the same column)\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_render","page":"Customization and Defaults","title":"RegressionTables.default_render","text":"default_render(rrs)\n\nDefaults to AsciiTable(), any concrete AbstractRenderType is allowed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_file","page":"Customization and Defaults","title":"RegressionTables.default_file","text":"default_file(render::AbstractRenderType, renderSettings::Tuple{<:AbstractRenderType, String}, rrs)\n\nDefaults to nothing, which means no file is saved.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_fe_suffix","page":"Customization and Defaults","title":"RegressionTables.default_print_fe_suffix","text":"default_print_fe_suffix(render::AbstractRenderType)\n\nWhether or not a suffix will be applied to the fixed effects, defaults to true.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_control_indicator","page":"Customization and Defaults","title":"RegressionTables.default_print_control_indicator","text":"default_print_control_indicator(render::AbstractRenderType)\n\nDefaults to true, which means if the regression has any variables ommitted (due to keep or drop), then a line is placed with Controls and Yes.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_standardize_coef","page":"Customization and Defaults","title":"RegressionTables.default_standardize_coef","text":"default_standardize_coef(render::AbstractRenderType, rrs)\n\nDefaults to false. Standardizing the coefficient divides the coefficient by its standard deviation and multiplies it by the standard deviation of the dependent variable. It is only possible for models that store the matrix, such as those in GLM.jl and MixedModels.jl. If it is not possible, the coefficients will not change.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_estimator","page":"Customization and Defaults","title":"RegressionTables.default_print_estimator","text":"default_print_estimator(render::AbstractRenderType, rrs)\n\nDefaults to true if more than one type of estimator is used. For example, if all regressions are \"OLS\", then this section will default to false, while if one regression is \"OLS\" and another is \"IV\", then this section will default to true.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_regression_statistics","page":"Customization and Defaults","title":"RegressionTables.default_regression_statistics","text":"default_regression_statistics(rr::RegressionModel)\n\nReturns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.\n\n\n\n\n\ndefault_regression_statistics(render::AbstractRenderType, rrs)\n\nDefaults to a union of the default_regression_statistics for each regression. For example, an \"OLS\" regression (with no fixed effects) will default to including [Nobs, R2], and a Probit regression will include [Nobs, PseudoR2], so the default will be [Nobs, R2, PseudoR2].\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_randomeffects","page":"Customization and Defaults","title":"RegressionTables.default_print_randomeffects","text":"default_print_randomeffects(render::AbstractRenderType, rrs)\n\nDefaults to true, but the section will not be printed if there are not random effects.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_clusters","page":"Customization and Defaults","title":"RegressionTables.default_print_clusters","text":"default_print_clusters(render::AbstractRenderType, rrs)\n\nDefaults to false, which means no cluster information is printed.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_use_relabeled_values","page":"Customization and Defaults","title":"RegressionTables.default_use_relabeled_values","text":"default_use_relabeled_values(render::AbstractRenderType, rrs) = true\n\nDefaults to true, which means the keep, drop and order arguments will use the relabeled values instead of the original values.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_confint_level","page":"Customization and Defaults","title":"RegressionTables.default_confint_level","text":"default_confint_level(render::AbstractRenderType, rrs)\n\nDefaults to 0.95, which means the 95% confidence interval is printed below the coefficient.\n\n\n\n\n\n","category":"function"},{"location":"customization/#Other-Defaults","page":"Customization and Defaults","title":"Other Defaults","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"While the user can adjust almost any part of this package (see How Types are Displayed), here are the remaining major defaults that are settable.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_breaks\nRegressionTables.default_symbol\nRegressionTables.interaction_combine\nRegressionTables.categorical_equal\nRegressionTables.random_effect_separator\nRegressionTables.estim_decorator\nRegressionTables.below_decoration\nRegressionTables.number_regressions_decoration\nRegressionTables.fe_suffix\nRegressionTables.wrapper\nRegressionTables.fe_value\nRegressionTables.cluster_suffix","category":"page"},{"location":"customization/#RegressionTables.default_breaks","page":"Customization and Defaults","title":"RegressionTables.default_breaks","text":"default_breaks(render::AbstractRenderType)\n\nThe default cutoffs for the number of \"*\" (or other value set by default_symbol). The default is [0.001, 0.01, 0.05], corresponding to a p-value of 0.1%, 1%, and 5%.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_symbol","page":"Customization and Defaults","title":"RegressionTables.default_symbol","text":"default_symbol(render::AbstractRenderType)\n\nThe default symbol to use for the p-value. The default is '*'.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.interaction_combine","page":"Customization and Defaults","title":"RegressionTables.interaction_combine","text":"interaction_combine(render::AbstractRenderType) = \" & \"\ninteraction_combine(render::AbstractLatex) = \" \\$\\times\\$ \"\ninteraction_combine(render::AbstractHtml) = \" × \"\n\nUsed to separate pieces of InteractedCoefName and defaults to:\n\n\" & \" in the general case\n\"$ \\times $\" in Latex\n\" × \" in HTML\n\nChange this by rerunning the function with the desired default, for example:\n\nRegressionTables.interaction_combine(render::AbstractLatex) = \" \\& \"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.categorical_equal","page":"Customization and Defaults","title":"RegressionTables.categorical_equal","text":"categorical_equal(render::AbstractRenderType)\n\nUsed to separate the name and level of CategoricalCoefName and defaults to \": \".\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.random_effect_separator","page":"Customization and Defaults","title":"RegressionTables.random_effect_separator","text":"random_effect_separator(render::AbstractRenderType)\n\nUsed to separate the left and right hand side of RandomEffectCoefName and defaults to \" | \".\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.estim_decorator","page":"Customization and Defaults","title":"RegressionTables.estim_decorator","text":"estim_decorator(render::AbstractRenderType, s, pval; breaks=default_breaks(render), sym=default_symbol(render))\n\nDecorates a value with a symbol based on p-value. In many journals, the symbol is a * and the p-value has three cutoffs, either 0.001, 0.01, and 0.05 or 0.01, 0.05, and 0.10.\n\nIt is possible to wrap the symbols in an additional element, for example, in Latex it is common to wrap the symbol as a superscript. To do so in all tables, run\n\nRegressionTables.wrapper(::AbstractLatex, deco) = \"\\$^{$deco}\\$\"\n\nIt is also possible to change the cutoffs or symbols. To change the cutoffs, run\n\nRegressionTables.default_breaks(::AbstractLatex) = [0.01, 0.05, 0.10] # make sure these are in order\n\nAnd to change the cutoffs run\n\nRegressionTables.default_symbol(::AbstractLatex) = \"x\" # or whatever you want\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.below_decoration","page":"Customization and Defaults","title":"RegressionTables.below_decoration","text":"below_decoration(render::AbstractRenderType, s)\n\nUsed to decorate a string below the main string and defaults to \"($s)\". Change this by running:\n\nRegressionTables.below_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.number_regressions_decoration","page":"Customization and Defaults","title":"RegressionTables.number_regressions_decoration","text":"number_regressions_decoration(render::AbstractRenderType, s)\n\nUsed to decorate the regression number (e.g., \"(1)\") and defaults to \"($s)\". Change this by running:\n\nRegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.fe_suffix","page":"Customization and Defaults","title":"RegressionTables.fe_suffix","text":"fe_suffix(render::AbstractRenderType)\n\nUsed to add a suffix to the fixed effects and defaults to \" Fixed Effects\". Change this by running:\n\nRegressionTables.fe_suffix(render::AbstractRenderType) = \" Fixed Effects\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.wrapper","page":"Customization and Defaults","title":"RegressionTables.wrapper","text":"wrapper(render::AbstractRenderType, s)\n\nUsed to wrap a string, particularly for estim_decorator and defaults to s (i.e., no wrapper). For example, to wrap the stars in Latex, run:\n\nRegressionTables.wrapper(::AbstractLatex, deco) = \"$^{$deco}$\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.fe_value","page":"Customization and Defaults","title":"RegressionTables.fe_value","text":"fe_value(render::AbstractRenderType, v)\n\nDetermines how to render a yes/no value for fixed effects, defaults to \"Yes\" and \"\". Can be changed by running:\n\nRegressionTables.fe_value(render::AbstractRenderType, v) = v ? \"Yes\" : \"No\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.cluster_suffix","page":"Customization and Defaults","title":"RegressionTables.cluster_suffix","text":"cluster_suffix(render::AbstractRenderType)\n\nUsed to add a suffix to the clustering and defaults to \" Clustering\". Change this by running:\n\nRegressionTables.cluster_suffix(render::AbstractRenderType) = \" Clustering\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#Labels","page":"Customization and Defaults","title":"Labels","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Labels are customizable by running the function that defines them. This makes it possible to change the labels once and then not worry about them on subsequent tables. To change a label, run:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label(render::AbstractRenderType, ::Type{Nobs}) = \"Obs.\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Labels use the Julia type system, so it is possible to create different labels depending on the table type. This is done by default for cases such as Nobs and R2, where the defaults for Latex and HTML are different. In such cases, it is necessary to set the label for all types that are used:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label(render::AbstractLatex, ::Type{Nobs}) = \"Obs.\"\nRegressionTables.label(render::AbstractHtml, ::Type{Nobs}) = \"Obs.\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Some labels (notably the R2 group), call another label function. For example:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"label(render::AbstractRenderType, ::Type{AdjR2}) = \"Adjusted \" * label(render, R2)","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Ths means that the label for AdjR2 relies on the label for R2. This also means that changing the label for R2 will change the labels for all other R2 types.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label\nRegressionTables.label_p\nRegressionTables.label_ols\nRegressionTables.label_iv\nRegressionTables.label_distribution","category":"page"},{"location":"customization/#RegressionTables.label","page":"Customization and Defaults","title":"RegressionTables.label","text":"label(render::AbstractRenderType, x::Type{Nobs}) = \"N\"\nlabel(render::AbstractLatex, x::Type{Nobs}) = \"\\$N\\$\"\nlabel(render::AbstractHtml, x::Type{Nobs}) = \"N\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2}) = \"R2\"\nlabel(render::AbstractLatex, x::Type{R2}) = \"\\$R^2\\$\"\nlabel(render::AbstractHtml, x::Type{R2}) = \"R2\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2McFadden}) = \"Pseudo \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2CoxSnell}) = \"Cox-Snell \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Nagelkerke}) = \"Nagelkerke \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Deviance}) = \"Deviance \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2}) = \"Adjusted \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2McFadden}) = \"Pseudo \" * label(render, AdjR2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2Deviance}) = \"Deviance \" * label(render, AdjR2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{DOF}) = \"Degrees of Freedom\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{LogLikelihood}) = \"Log Likelihood\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AIC}) = \"AIC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AICC}) = \"AICC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{BIC}) = \"BIC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStat}) = \"F\"\nlabel(render::AbstractLatex, x::Type{FStat}) = \"\\$F\\$\"\nlabel(render::AbstractHtml, x::Type{FStat}) = \"F\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatPValue}) = label(render, FStat) * \"-test \" * label_p(render) * \" value\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatIV}) = \"First-stage \" * label(render, FStat) * \" statistic\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatIVPValue}) = \"First-stage \" * label_p(render) * \" value\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Within}) = \"Within \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{RegressionType}) = \"Estimator\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{HasControls}) = \"Controls\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{RegressionNumbers}) = \"\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_p","page":"Customization and Defaults","title":"RegressionTables.label_p","text":"label_p(render::AbstractRenderType) = \"p\"\nlabel_p(render::AbstractLatex) = \"\\$p\\$\"\nlabel_p(render::AbstractHtml) = \"p\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_ols","page":"Customization and Defaults","title":"RegressionTables.label_ols","text":"label_ols(render::AbstractRenderType) = \"OLS\"\n\nAlso see RegressionType\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_iv","page":"Customization and Defaults","title":"RegressionTables.label_iv","text":"label_iv(render::AbstractRenderType) = \"IV\"\n\nAlso see RegressionType\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_distribution","page":"Customization and Defaults","title":"RegressionTables.label_distribution","text":"label_distribution(render::AbstractRenderType, d::D) where {D <: UnivariateDistribution} = string(Base.typename(D).wrapper)\nlabel_distribution(render::AbstractRenderType, d::NegativeBinomial) = \"Negative Binomial\"\nlabel_distribution(render::AbstractRenderType, d::InverseGaussian) = \"Inverse Gaussian\"\n\nHow to label non-linear regressions and defaults to the name of the distribution. For example, Probit will be \"Probit\". Two exceptions are given for NegativeBinomial and InverseGaussian, which are \"Negative Binomial\" and \"Inverse Gaussian\", respectively. This can be changed for a specific distribution by running:\n\nRegressionTables.label_distribution(render::AbstractRenderType, d::Poisson) = \"Poisson\"\n\n\n\n\n\n","category":"function"},{"location":"api/#API-of-Underlying-Functions","page":"API","title":"API of Underlying Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Pages=[\"api.md\"]\nDepth = 3","category":"page"},{"location":"api/#DataRow","page":"API","title":"DataRow","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.DataRow","category":"page"},{"location":"api/#RegressionTables.DataRow","page":"API","title":"RegressionTables.DataRow","text":"mutable struct DataRow{T<:AbstractRenderType} <: AbstractVector{String}\n data::Vector\n align::String\n colwidths::Vector{Int}\n print_underlines::Vector{Bool}\n render::T\nend\n\nDataRow(x::DataRow) = x\n\n(::Type{T})(x::DataRow) where {T<:AbstractRenderType} = DataRow(x.data, x.align, x.colwidths, x.print_underlines, T())\n\nfunction DataRow(\n data::Vector,\n align,\n colwidths,\n print_underlines,\n render::AbstractRenderType;\n combine_equals=false\n)\n\nfunction DataRow(\n data::Vector;\n align=\"l\" * \"r\" ^ (length(data) - 1),\n colwidths=fill(0, length(data)),\n print_underlines=zeros(Bool, length(data)),\n render::AbstractRenderType=AsciiTable(),\n combine_equals=false\n)\n\nDataRow forms the fundamental element of a RegressionTable. For a user, these can be passed as additional elements to group or extralines in regtable. A DataRow is typed with an AbstractRenderType, which controls how the DataRow is displayed. The default is AsciiTable. The DataRow contains four other elements:\n\ndata::Vector: The data to be displayed in the row. Can be individual elements (e.g., [1, 2, 3]) or pairs with a range (e.g., [1 => 1:2, 2 => 3:4]), or a combination of the two.\nalign::String: A string of ('l', 'r', 'c'), one for each element of data, indicating that elements alignment.\ncolwidths::Vector{Int}: A vector of integers, one for each element of data, indicating the width of the column. Can calculate the widths automatically using calc_widths and update them with update_widths!.\nprint_underlines::Vector{Bool}: A vector of booleans, one for each element of data, indicating whether to print an underline under the element. This is useful for printing the header of a table.\n\nDataRow is a subtype of AbstractVector{String}. It implements getindex and setindex!, which allows changing of individual elements.\n\nnote: Note\nIn most cases, it is not necessary to specify the render type for DataRow. While constructing a RegressionTable, the render type is changed to the render type of the RegressionTable.\n\nExamples\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5])\n Group 1 Group 2\n\njulia> DataRow([\"\", \"Group 1\", \"Group 1\", \"Group 2\", \"Group 2\"]; combine_equals=true)\n Group 1 Group 2\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5])\n Group 1 Group 2\n\njulia> DataRow([\" \", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; print_underlines=[false, false, true])\n Group 1 Group 2\n -------\n\njulia> DataRow([\"Group 0\", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; colwidths=[20, 20, 20], align=\"lcr\", print_underlines=true)\nGroup 0 Group 1 Group 2\n-------------------- -------------------- --------------------\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; render=LatexTable())\n & \\multicolumn{2}{r}{Group 1} & \\multicolumn{2}{r}{Group 2} \\\\ \n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTable","page":"API","title":"RegressionTable","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.RegressionTable","category":"page"},{"location":"api/#RegressionTables.RegressionTable","page":"API","title":"RegressionTables.RegressionTable","text":"mutable struct RegressionTable{T<:AbstractRenderType} <: AbstractMatrix{String}\n data::Vector{DataRow{T}}\n align::String\n render::T\n breaks::Vector{Int}\n colwidths::Vector{Int}\n vertical_gaps::Vector{Int}\nend\n\nRegressionTable(header::Vector, body::Matrix, args...; vargs...)\nRegressionTable(\n header::Matrix,\n body::Matrix,\n render::T=AsciiTable();\n breaks=[size(header, 1)],\n align='l' * 'r' ^ (size(header, 2) - 1),\n colwidths=fill(0, size(header, 2)),\n header_align='l' * 'c' ^ (size(header, 2) - 1),\n extralines::Vector = DataRow[]\n) where T<:AbstractRenderType\n\nThe general container. This provides some general information about the table. The DataRow handles the main printing, but this provides other necessary information, especially the break field, which is used to determine where to put the lines (e.g., \\midrule in LaTeX).\n\ndata: A vector of DataRow objects.\nalign: A string of characters, one for each column, indicating the alignment of the column. The characters are l for left, c for center, and r for right.\nrender: The render type of the table. This must be the same as the render type of the DataRow objects and is used for convenience.\nbreaks: A vector of integers, indicating where to put the lines (e.g., \\midrule in LaTeX). When displayed, the break will be placed after the line number is printed (breaks = [5] will print a break after the 5th line is printed).\ncolwidths: A vector of integers, one for each column, indicating the width of the column. Can calculate the widths automatically using calc_widths and update them with update_widths!.\nvertical_gaps: A vector of integers, indicating where to put vertical gaps in the table. These are places where two underlined cells are not connected. This is necessary for Excel integration.\n\nThe RegressionTable is a subtype of the AbstractMatrix{String} type (though this functionality is somewhat experimental). Importantly, this implements getindex and setindex!, which means individual elements of the table can be modified after its construction. See examples below. It also allows a RegressionTable to be passed to a function that expects a matrix, for example, exporting to a DataFrame DataFrames.jl would be DataFrame(Matrix(tab), :auto). Note that this will not keep multicolumn, unerlines, etc. information, but could be useful for exporting to other formats.\n\nThis type also has two convenience constructors which might be helpful if using this package to print summary statistics.\n\nExample\n\njulia> df = RDatasets.dataset(\"datasets\", \"iris\");\n\njulia> df = describe(df, :mean, :std, :q25, :median, :q75; cols=[\"SepalLength\", \"SepalWidth\", \"PetalLength\", \"PetalWidth\"]);\n\njulia> t = RegressionTables.RegressionTable(names(df), Matrix(df))\n \n----------------------------------------------------\nvariable mean std q25 median q75\n----------------------------------------------------\nSepalLength 5.843 0.828 5.100 5.800 6.400\nSepalWidth 3.057 0.436 2.800 3.000 3.300\nPetalLength 3.758 1.765 1.600 4.350 5.100\nPetalWidth 1.199 0.762 0.300 1.300 1.800\n----------------------------------------------------\n\njulia> t[1, 2] = \"Mean of Variable\";\n\njulia> t[2, 3] = 0;\n\njulia> RegressionTables.update_widths!(t); # necessary if a column gets longer\n\njulia> t\n \n---------------------------------------------------------------\nvariable Mean of Variable std q25 median q75\n---------------------------------------------------------------\nSepalLength 5.843 0 5.100 5.800 6.400\nSepalWidth 3.057 0.436 2.800 3.000 3.300\nPetalLength 3.758 1.765 1.600 4.350 5.100\nPetalWidth 1.199 0.762 0.300 1.300 1.800\n---------------------------------------------------------------\n\n\n\n\n\n","category":"type"},{"location":"api/#Render-Types","page":"API","title":"Render Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractRenderType","category":"page"},{"location":"api/#RegressionTables.AbstractRenderType","page":"API","title":"RegressionTables.AbstractRenderType","text":"abstract type AbstractRenderType end\n\nThe generic top level type for render types. Most defaults are set here unless there is a reason it needs to be set at a lower level. Subtypes are still abstract types and include AbstractAscii, AbstractLatex, and AbstractHtml.\n\n\n\n\n\n","category":"type"},{"location":"api/#Ascii","page":"API","title":"Ascii","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractAscii\nRegressionTables.AsciiTable","category":"page"},{"location":"api/#RegressionTables.AbstractAscii","page":"API","title":"RegressionTables.AbstractAscii","text":"abstract type AbstractAscii <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractAscii, so new tables (with different defaults) can be created by subtyping AbstractAscii with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.AsciiTable","page":"API","title":"RegressionTables.AsciiTable","text":"struct AsciiTable <: AbstractAscii end\n\nThe main concrete type for AbstractAscii. This is the default type used for plain text rendering.\n\n\n\n\n\n","category":"type"},{"location":"api/#Latex","page":"API","title":"Latex","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractLatex\nRegressionTables.LatexTable\nRegressionTables.LatexTableStar","category":"page"},{"location":"api/#RegressionTables.AbstractLatex","page":"API","title":"RegressionTables.AbstractLatex","text":"abstract type AbstractLatex <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractLatex, so new tables (with different defaults) can be created by subtyping AbstractLatex with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.LatexTable","page":"API","title":"RegressionTables.LatexTable","text":"struct LatexTable <: AbstractLatex end\n\nThe main concrete type for AbstractLatex. This type is used to create Latex tables.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.LatexTableStar","page":"API","title":"RegressionTables.LatexTableStar","text":"struct LatexTableStar <: AbstractLatex end\n\nAn alternative concrete type for AbstractLatex. This type is used to create Latex tables that span the entire text width.\n\n\n\n\n\n","category":"type"},{"location":"api/#HTML","page":"API","title":"HTML","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractHtml\nRegressionTables.HtmlTable","category":"page"},{"location":"api/#RegressionTables.AbstractHtml","page":"API","title":"RegressionTables.AbstractHtml","text":"abstract type AbstractHtml <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractHtml, so new tables (with different defaults) can be created by subtyping AbstractHtml with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.HtmlTable","page":"API","title":"RegressionTables.HtmlTable","text":"struct HtmlTable <: AbstractHtml end\n\nThe main concrete type for AbstractHtml. This type is used to create HTML tables.\n\n\n\n\n\n","category":"type"},{"location":"api/#Coefficient-Naming","page":"API","title":"Coefficient Naming","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractCoefName\nRegressionTables.get_coefname","category":"page"},{"location":"api/#RegressionTables.AbstractCoefName","page":"API","title":"RegressionTables.AbstractCoefName","text":"abstract type AbstractCoefName end\n\nThese names largely mirror their equivalents in StatsModels.jl. The main difference here is that the names are always based on strings (instead of symbols). There are also several default functions that are resused (e.g., get and replace) to make relabeling coefficients easier.\n\nAbstractCoefName simply acts as a parent type to the other types. The other types are:\n\nCoefName: for Term, ContinuousTerm and FunctionTerm\nInteractedCoefName: for InteractionTerm\nCategoricalCoefName: for CategoricalTerm\nInterceptCoefName: for ConstantTerm\n\nUsing the function get_coefname will return the appropriate type for the term.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.get_coefname","page":"API","title":"RegressionTables.get_coefname","text":"get_coefname(x::AbstractTerm)::CoefName\nget_coefname(x::Term)::CoefName\nget_coefname(x::InteractionTerm)::InteractedCoefName\nget_coefname(x::InterceptTerm{H})::InterceptCoefName\nget_coefname(x::CategoricalTerm)::CategoricalCoefName\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractCoefName && t != RegressionTables.AbstractCoefName","category":"page"},{"location":"api/#RegressionTables.CategoricalCoefName","page":"API","title":"RegressionTables.CategoricalCoefName","text":"struct CategoricalCoefName <: AbstractCoefName\n name::String\n level::String\nend\n\nUsed to store the name of a coefficient for a CategoricalTerm. The level is the level of the categorical. In other words, the name is the column name, the level is the category within that column.\n\nIn a regression, the display of categorical terms is typically displayed as \"name: level\". You can change how the categorical term is \"equal\" by changing the categorical_equal function. The default is \": \", but you can change: this by setting:\n\nRegressionTables.categorical_equal(render::AbstractRenderType) = \" = \"\nRegressionTables.categorical_equal(render::AbstractLatex) = \" $=$ \"\n\nYou can also change how the categorical term is displayed by changing the repr function. The default is:\n\nBase.repr(render::AbstractRenderType, x::RegressionTables.CategoricalCoefName; args...) =\n \"$(RegressionTables.value(x))$(RegressionTables.categorical_equal(render)) $(x.level)\"\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.CoefName","page":"API","title":"RegressionTables.CoefName","text":"struct CoefName <: AbstractCoefName\n name::String\nend\n\nUsed to store the name of a coefficient. This is used for Term, ContinuousTerm and FunctionTerm.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.FixedEffectCoefName","page":"API","title":"RegressionTables.FixedEffectCoefName","text":"struct FixedEffectCoefName <: AbstractCoefName\n name::AbstractCoefName\nend\n\nUsed to store the name of a coefficient for a FixedEffectTerm. The name is the name of the fixed effect. This allows a suffix to be applied later.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.InteractedCoefName","page":"API","title":"RegressionTables.InteractedCoefName","text":"struct InteractedCoefName <: AbstractCoefName\n names::Vector\nend\n\nUsed to store the different coefficient names that makes up an InteractionTerm. The internals of the vector are typically CoefName, but can also be strings.\n\nIn a regression, each element of the vector is typically displayed as \"name1 & name2 & ...\" (in AsciiTables). The separator is set by interaction_combine function, and the default varies based on AbstractRenderType:\n\nFor AbstractAscii, it defaults to \" & \"\nFor AbstractLaTeX, it defaults to \" $\\times$ \"\nFor AbstractHtml, it defaults to \" × \"\n\nYou can change the separator by running:\n\nRegressionTables.interaction_combine(render::$RenderType) = \" & \"\n\nwhere $RenderType is the type of the renderer you want to change. For example, to change the output in AbstractLaTeX:\n\nRegressionTables.interaction_combine(::AbstractLaTeX) = \" \\& \"\n\nYou can control how interaction terms are displayed more generally by changing:\n\nBase.repr(render::AbstractRenderType, x::RegressionTables.InteractedCoefName; args...) =\n join(RegressionTables.value.(x), RegressionTables.interaction_combine(render))\n\nSee Customization of Defaults for more details.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.InterceptCoefName","page":"API","title":"RegressionTables.InterceptCoefName","text":"struct InterceptCoefName <: AbstractCoefName end\n\nUsed as a simple indicator for the existence of an intercept term. This allows relabeling of the intercept in all cases by setting\n\nRegressionTables.label(::InterceptCoefName) = \"My Intercept\"\n\nSee Customization of Defaults for more details.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.RandomEffectCoefName","page":"API","title":"RegressionTables.RandomEffectCoefName","text":"struct RandomEffectCoefName <: AbstractCoefName\n rhs::CoefName\n lhs::AbstractCoefName\n val::Float64\nend\n\nUsed to store the name and the standard deviation of a coefficient for a RandomEffectTerm from MixedModels.jl. The standard deviation is stored since that is often the useful information on the relationship between rhs and lhs.\n\n\n\n\n\n","category":"type"},{"location":"api/#Utility-Functions","page":"API","title":"Utility Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.calc_widths\nRegressionTables.update_widths!\nRegressionTables.value_pos\nRegressionTables.combine_other_statistics\nRegressionTables.combine_statistics\nRegressionTables.build_nm_list\nRegressionTables.reorder_nms_list\nRegressionTables.drop_names!\nRegressionTables.add_blank\nRegressionTables.missing_vars\nRegressionTables.add_element!\nRegressionTables.find_vertical_gaps\nRegressionTables.extra_cell_space","category":"page"},{"location":"api/#RegressionTables.calc_widths","page":"API","title":"RegressionTables.calc_widths","text":"calc_widths(rows::Vector{DataRow{T}}) where {T<:AbstractRenderType}\n\nCalculate the widths of each column in the table. For rows with multicolumn cells, the width of the multicolumn is divided evenly among the columns it spans.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.update_widths!","page":"API","title":"RegressionTables.update_widths!","text":"update_widths!(row::DataRow{T}, new_lengths=length.(repr.(T(), row.data))) where {T}\n\nUpdates the widths of each column in the row. If lengths are provided, then it should equate to the total number of columns in the table, not the number of elements in the row.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.value_pos","page":"API","title":"RegressionTables.value_pos","text":"value_pos(nms, x::String)\n\nReturns the position of the string x in the vector of strings or AbstractCoefName nms.\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, \"coef1\")\n1:1\n\njulia> value_pos(nms, \"coef2\")\n2:2\n\njulia> value_pos(nms, \"coef3\")\nInt64[]\n\njulia> value_pos(nms, \"coef3 & coef4\")\n4:4\n\njulia> value_pos(nms, \"(Intercept)\")\n3:3\n\njulia> value_pos(nms, \"coef1 & coef3\")\n5:5\n\njulia> value_pos(nms, \"coef5: 10\")\n6:6\n\n\n\n\n\nvalue_pos(nms, x::Int)\n\nChecks that x is a valid index for the vector of strings or AbstractCoefName nms and returns a range of x:x\n\n\n\n\n\nvalue_pos(nms, x::UnitRange)\n\nChecks that x is a valid index for the vector of strings or AbstractCoefName nms and returns x\n\n\n\n\n\nvalue_pos(nms, x::BitVector)\n\nReturns a vector of indices where x is true, called by the regex version of value_pos.\n\n\n\n\n\nvalue_pos(nms, x::Regex)\n\nLooks within each element of the vector nms (which is either a string or an AbstractCoefName) and returns a vector of indices where the regex x is found.\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, r\"coef1\") == [1, 5]\ntrue\n\njulia> value_pos(nms, r\"coef[1-3]\") == [1, 2, 4, 5]\ntrue\n\njulia> value_pos(nms, r\"coef[1-3] & coef4\") == [4]\ntrue\n\njulia> value_pos(nms, r\" & \") == [4, 5]\ntrue\n\njulia> value_pos(nms, r\"coef5\") == [6, 7]\ntrue\n\njulia> value_pos(nms, r\"coef5: 10\") == [6]\ntrue\n\n\n\n\n\nvalue_pos(nms, x::Nothing)\n\nReturns an empty vector, called by the regex and string version of value_pos.\n\n\n\n\n\nvalue_pos(nms, x::Symbol)\n\nExpects a symbol of the form :last or :end and returns the last value of nms, both are included for consistency with the Tuple\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, :last)\n7:7\n\njulia> value_pos(nms, :end)\n7:7\n\n\n\n\n\nvalue_pos(nms, x::Tuple{Symbol, Int})\n\nExpects a tuple of the form (:last, n) or (:end, n). (:last, n) returns the last n values (1:5 with (:last, 2) is 4:5), while (:end, n) returns the last index minus n (1:5 with (:end, 2) is 3).\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, (:last, 2))\n6:7\n\njulia> value_pos(nms, (:end, 2))\n5:5\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.combine_other_statistics","page":"API","title":"RegressionTables.combine_other_statistics","text":"combine_other_statistics(combine_other_statistics(\n stats;\n fill_val=missing,\n print_fe_suffix=true,\n fixedeffects=Vector{String}(),\n labels=Dict{String, String}(),\n transform_labels=Dict{String, String}(),\n kwargs...\n)\n\nTakes vector of nothing or statistics and combines these into a single section. These stats should be a vector of pairs, so this function expects a vector of these vector pairs or nothing. The function will combine the pairs into a single matrix. The first matrix column is a list of unique names which are the first value of the pairs and the rest of the matrix is the last value of the pairs organized.\n\nArguments\n\nstats is a Vector of Vector{Pair} or nothing that should be combined.\nfill_val defaults to missing but is the default value if the statistic is not present in that regression. For example, with fixed effects this defaults to FixedEffectValue(false) if the fixed effect is not present in the regression.\nfixedeffects is a Vector of fixed effects to include in the table. Defaults to Vector{String}(), which means all fixed effects are included. Can also be regex, integers or ranges to select which fixed effects\nprint_fe_suffix is a Bool that governs whether the fixed effects should be printed with a suffix. Defaults to true, which means the fixed effects will be printed as $X Fixed Effects. If false, the fixed effects will just be the fixed effect ($X).\nlabels is the labels from the main regtable, used to change the names of the names.\ntransform_labels is the transform_labels from the main regtable.\nkwargs are any other kwargs passed to regtable, allowing for flexible arguments for truly custom type naming.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.combine_statistics","page":"API","title":"RegressionTables.combine_statistics","text":"combine_statistics(tables, stats)\n\nTakes a set of tables (RegressionModels) and a vector of AbstractRegressionStatistic. The stats argument can also be a pair of AbstractRegressionStatistic => String, which uses the second value as the name of the statistic in the final table.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.build_nm_list","page":"API","title":"RegressionTables.build_nm_list","text":"build_nm_list(nms, keep)\n\nTakes the list of strings or AbstractCoefName nms and returns a subset of nms that contains only the elements in keep. Will also reorder the elements that are kept based on the order of keep.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.reorder_nms_list","page":"API","title":"RegressionTables.reorder_nms_list","text":"reorder_nms_list(nms, order)\n\nReorders the vector of strings or AbstractCoefName nms according to the order provided. All elements of nms are kept.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.drop_names!","page":"API","title":"RegressionTables.drop_names!","text":"drop_names!(nms, to_drop)\n\nDrops the elements of nms that are in to_drop. Does not reorder any other elements.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.add_blank","page":"API","title":"RegressionTables.add_blank","text":"add_blank(groups::Matrix, n)\nadd_blank(groups::Vector{Vector}, n)\n\nRecursively checks whether the number of columns in groups (or the length of the vector groups) is less than n, if so, add a blank column (or element) to the left of the matrix (first element of the vector).\n\nThis is used to make sure a provided piece of data is at least n columns, fitting into the table.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.missing_vars","page":"API","title":"RegressionTables.missing_vars","text":"missing_vars(table::RegressionModel, coefs::Vector)\n\nChecks whether any of the coefficients in table are not in coefs, returns true if so, false otherwise.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.add_element!","page":"API","title":"RegressionTables.add_element!","text":"add_element!(row::DataRow, val, align_i, colwidth_i, print_underline_i, i)\n\nAdds an element to the row and is only called when combine_equals=true. This means that if the last element in the row is the same as the provided val, then the last element is extended to include the new column.\n\n\n\n\n\nadd_element!(row::DataRow, val::Pair, align_i, colwidth_i, print_underline_i, i)\n\nAdds an element to the row and is only called when combine_equals=true. Since val is a pair, is is simply added to the row as is, this allows for the creation of two multicolumn cells that have the same information but are separate.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.find_vertical_gaps","page":"API","title":"RegressionTables.find_vertical_gaps","text":"find_vertical_gaps(data::Vector{DataRow{T}}) where T\n\nFinds locations in a vector of DataRow objects where there are vertical gaps. A \"vertical gap\" is a place where two underlined cells are not connected. If there is a gap, then there needs to be a space between the current column and the next column. This makes it unnecessary to have a final vertical gap since the table ends there.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.extra_cell_space","page":"API","title":"RegressionTables.extra_cell_space","text":"extra_cell_space(render::AbstractRenderType)\n\nUsed to add extra space to the cell and defaults to 0.\n\n\n\n\n\n","category":"function"},{"location":"api/#How-Types-are-Displayed","page":"API","title":"How Types are Displayed","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"This section describes how different types are displayed. Throughout this package, repr(T(), x) where T is a concrete type of AbstractRenderType is used to convert something to a string. This allows two things.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Since it is easy to create new AbstractRenderTypes, it is possible to create customized displays for almost anyway situation\nSince most things in this package are types, each can be customized","category":"page"},{"location":"api/","page":"API","title":"API","text":"Base.repr","category":"page"},{"location":"api/#Base.repr","page":"API","title":"Base.repr","text":"Base.repr(render::AbstractRenderType, x; args...)\n\nWill render x as a string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Pair; args...)\n\nBy default, will render the first element of the pair according to the render type. In cases of AbstractLatex or AbstractHtml, uses the second element to determine number of columns to span.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Int; args...)\n\nBy default, will render the integer with commas\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Float64; digits=default_digits(render, x), commas=true, str_format=nothing, args...)\n\nBy default, will render the float with commas and the default number of digits. If str_format is specified, will use that instead of digits and commas\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Nothing; args...)\n\nBy default, will render the nothing as an empty string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Missing; args...)\n\nBy default, will render the missing as an empty string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractString; args...)\n\nBy default, will render the string as is\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Bool; args...)\n\nBy default, will render the boolean as \"Yes\" or \"\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractRegressionStatistic; digits=default_digits(render, x), args...)\n\nBy default, will render the statistic with commas and the default number of digits\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractR2; digits=default_digits(render, x), args...)\n\nBy default, will render the same as AbstractRegressionStatistic\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractUnderStatistic; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses (1.234)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::ConfInt; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses (1.234, 5.678)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::CoefValue; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses and will call estim_decorator for the decoration\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: AbstractRegressionStatistic}\n\nBy default, will call the label function related to the type\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{RegressionType}; args...)\n\nBy default, will call the label function related to the RegressionType\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Tuple; args...)\n\nBy default, will render the tuple with spaces between the elements\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractCoefName; args...)\n\nBy default, will render the name of the coefficient, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::FixedEffectCoefName; args...)\n\nBy default, will render the coefficient and add \" Fixed Effects\" as a suffix, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)\n\nBy default, will render the coefficient and add the \" Clustering\" function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InteractedCoefName; args...)\n\nBy default, will render the coefficient and add the interaction_combine function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::CategoricalCoefName; args...)\n\nBy default, will render the coefficient and add the categorical_equal function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InterceptCoefName; args...)\n\nBy default, will render the coefficient as (Intercept), also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::HasControls; args...)\n\nBy default, will render the same as Bool (\"Yes\" and \"\")\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RegressionNumbers; args...)\n\nBy default, will render the number in parentheses (e.g., \"(1)\", \"(2)\"...)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: HasControls}\n\nBy default, will call the label function related to the type\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RegressionType; args...)\n\nBy default, will check if the RegressionType is an instrumental variable regression and call the label function related to the type. If it is an instrumental variable, will then call label_iv, otherwise it will call the related distribution.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::D; args...) where {D <: UnivariateDistribution}\n\nWill print the distribution as is (e.g., Probit will be \"Probit\"), unless another function overrides this behavior (e.g., Normal, InverseGaussian, NegativeBinomial)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InverseGaussian; args...)\n\nBy default, will be \"Inverse Gaussian\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::NegativeBinomial; args...)\n\nBy default, will be \"Negative Binomial\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Normal; args...)\n\nBy default, will call label_ols\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)\n\nHow to render a RandomEffectCoefName and defaults to the right hand side, then the separator, then the left hand side.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::FixedEffectValue; args...)\n\nHow to render a FixedEffectValue and defaults to calling fe_value\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::ClusterValue; args...)\n\nHow to render a ClusterValue and defaults to how true and false is displayed.\n\nIf wanting to show the size of the clusters, run:\n\nRegressionTables.repr(render::AbstractRenderType, x::ClusterValue; args...) = repr(render, value(x); args...)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectValue; args...)\n\nHow to render a RandomEffectValue and defaults to calling another render function, dependent on the type of the value\n\n\n\n\n\n","category":"function"},{"location":"api/#New-RegressionModel-Types","page":"API","title":"New RegressionModel Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"This package is designed to be generally compatible with the RegressionModel abstraction. It has special conditions defined around four commonly used packages (FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl). It is possible to add new models to this list, either by creating an extension for this package or by creating the necessary items in an independent package.","category":"page"},{"location":"api/","page":"API","title":"API","text":"For any new RegressionModel, there may be a need to define the following functions for the package to work correctly. Many of these will work without any issues if following the StatsModels API, and many of the others are useful for customizing how the regression result is displayed. It is also possible to redefine how RegressionTables.AbstractRegressionStatistic are displayed.","category":"page"},{"location":"api/","page":"API","title":"API","text":"RegressionTables._formula\nRegressionTables._responsename\nRegressionTables._coefnames\nRegressionTables._coef\nRegressionTables._stderror\nRegressionTables._dof_residual\nRegressionTables._pvalue\nRegressionTables.other_stats\nRegressionTables.default_regression_statistics(::RegressionModel)\nRegressionTables.can_standardize\nRegressionTables.standardize_coef_values\nRegressionTables.RegressionType","category":"page"},{"location":"api/#RegressionTables._formula","page":"API","title":"RegressionTables._formula","text":"_formula(x::RegressionModel)\n\nGenerally a passthrough for the formula function from the StatsModels package. Note tha the formula function returns the FormulaSchema.\n\nThis function is only used internally in the RegressionTables._responsename and RegressionTables._coefnames functions. Therefore, if the RegressionModel uses those two functions without using formula, this function is not necessary.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._responsename","page":"API","title":"RegressionTables._responsename","text":"_responsename(x::RegressionModel)\n\nReturns the name of the dependent variable in the regression model. The default of this returns a AbstractCoefName object, but it can be a String or Symbol as well.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._coefnames","page":"API","title":"RegressionTables._coefnames","text":"_coefnames(x::RegressionModel)\n\nReturns a vector of the names of the coefficients in the regression model. The default of this returns a vector of AbstractCoefName objects, but it can be a vector of String or Symbol as well.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._coef","page":"API","title":"RegressionTables._coef","text":"_coef(x::RegressionModel)\n\nReturns a vector of the coefficients in the regression model. By default, is just a passthrough for the coef function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._stderror","page":"API","title":"RegressionTables._stderror","text":"_stderror(x::RegressionModel)\n\nReturns a vector of the standard errors of the coefficients in the regression model. By default, is just a passthrough for the stderror function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._dof_residual","page":"API","title":"RegressionTables._dof_residual","text":"_dof_residual(x::RegressionModel)\n\nReturns the degrees of freedom of the residuals in the regression model. By default, is just a passthrough for the dof_residual function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._pvalue","page":"API","title":"RegressionTables._pvalue","text":"_pvalue(x::RegressionModel)\n\nReturns a vector of the p-values of the coefficients in the regression model.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.other_stats","page":"API","title":"RegressionTables.other_stats","text":"other_stats(rr::RegressionModel, s::Symbol)\n\nReturns any other statistics to be displayed. This is used (if the appropriate extension is loaded) to display the fixed effects in a FixedEffectModel (or GLFixedEffectModel), clusters in those two, or Random Effects in a MixedModel. For other regressions, this returns nothing.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.default_regression_statistics-Tuple{RegressionModel}","page":"API","title":"RegressionTables.default_regression_statistics","text":"default_regression_statistics(rr::RegressionModel)\n\nReturns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.\n\n\n\n\n\n","category":"method"},{"location":"api/#RegressionTables.can_standardize","page":"API","title":"RegressionTables.can_standardize","text":"can_standardize(x::RegressionModel)\n\nReturns a boolean indicating whether the coefficients can be standardized. standardized coefficients are coefficients that are scaled by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.\n\nThis is only possible of the RegressionModel includes the model matrix or the standard deviation of the dependent variable. If the RegressionModel does not include either of these, then this function should return false.\n\nSee also RegressionTables.standardize_coef_values.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.standardize_coef_values","page":"API","title":"RegressionTables.standardize_coef_values","text":"standardize_coef_values(std_X, std_Y, val)\n\nStandardizes the coefficients by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.\n\nThis function is only used if the RegressionTables.can_standardize function returns true.\n\nArguments\n\nstd_X::Real: The standard deviation of the independent variable.\nstd_Y::Real: The standard deviation of the dependent variable.\nval::Real: The value to be standardized (either the coefficient or the standard error).\n\nnote: Note\nIf the standard deviation of the independent variable is 0, then the interpretation of the coefficient is how many standard deviations of the dependent variable away from 0 is the intercept. In this case, the function returns val / std_Y.Otherwise, the function returns val * std_X / std_Y.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.RegressionType","page":"API","title":"RegressionTables.RegressionType","text":"struct RegressionType{T}\n val::T\n is_iv::Bool\nend\n\nThe type of the regression. val should be a distribution from the Distributions.jl package. is_iv indicates whether the regression is an instrumental variable regression. The default label for the regression type is \"Estimator\". The labels for individual regression types (e.g., \"OLS\", \"Poisson\") can be set by running:\n\nRegressionTables.label_ols(render::AbstractRenderType) = $name\nRegressionTables.label_iv(render::AbstractRenderType) = $name\n\nOr for individual distributions by running:\n\nBase.repr(render::AbstractRenderType, x::$Distribution; args...) = $Name\n\n\n\n\n\n","category":"type"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"DocTestSetup = quote # hide\n using RegressionTables, DataFrames, FixedEffectModels, RDatasets\n\n df = RDatasets.dataset(\"datasets\", \"iris\")\n df[!,:SpeciesDummy] = df[!,:Species]\n\n rr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(SpeciesDummy)))\n rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(SpeciesDummy)))\n rr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(SpeciesDummy)))\n rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(SpeciesDummy)))\n rr5 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + fe(SpeciesDummy)))\n rr6 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + SpeciesDummy))\nend # hide","category":"page"},{"location":"keep_drop_order/#Keep-Drop-and-Order-Arguments","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Pages=[\"keep_order_drop.md\"]","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"the keep, drop and order keyword arguments act similarly and allow for quick selection of coefficients to keep and change. The sequence these options are applied in is keep -> drop -> order, so coefficients kept can then be reordered. There are four fundamental ways to select arguments:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"By string\nBy Regex\nBy index number\nBy range","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"note: Note\nkeep also adjusts the order of the coefficients, so specifying keep and then order would allow order to adjust the coefficients kept. drop does not change order of coefficients.","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"note: Note\nThe keep, drop and order arguments use the relabeled versions of coefficients. If labels (or transform_labels) are used, then the keep, drop and order arguments should use the relabeled versions of the coefficients.","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Just to setup these examples:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"using RegressionTables, DataFrames, FixedEffectModels, RDatasets\n\ndf = dataset(\"datasets\", \"iris\")\ndf[!,:SpeciesDummy] = df[!,:Species]\n\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(SpeciesDummy)))\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(SpeciesDummy)))\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(SpeciesDummy)))\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(SpeciesDummy)))\nrr5 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + fe(SpeciesDummy)))\nrr6 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + SpeciesDummy))","category":"page"},{"location":"keep_drop_order/#By-String","page":"Keep Drop and Order Arguments","title":"By String","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Selecting coefficients by string is the most straightforward. Specify arguments by exactly matching the output string (if labels are not used, these are the same as the input string). For example, starting with the original example:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4)\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"To select the first two coefficients, specify keep=[\"SepalWidth\", \"PetalLength\"]:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4; keep=[\"SepalWidth\", \"PetalLength\"])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Interacted coefficients are selected using the & to separate the interactions, even if the settings use a different interaction. For example, in Latex, the interaction defaults to \\$times\\$, but would still be selected by using &:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; render=LatexTable(), keep=[\"SepalWidth & PetalLength\"])\n\n# output\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\ \n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\ \n\\midrule\nSepalWidth $\\times$ PetalLength & & & -0.070 & \\\\ \n & & & (0.041) & \\\\ \n\\midrule\nSpeciesDummy Fixed Effects & Yes & Yes & Yes & Yes \\\\ \n\\midrule\nControls & Yes & Yes & Yes & Yes \\\\ \n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\ \n$R^2$ & 0.726 & 0.863 & 0.870 & 0.635 \\\\ \nWithin-$R^2$ & 0.281 & 0.642 & 0.659 & 0.391 \\\\ \n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Similar to interacted coefficients, categorical coefficients are selected using a : between the name and the category.","category":"page"},{"location":"keep_drop_order/#By-Regex","page":"Keep Drop and Order Arguments","title":"By Regex","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Regex allows the selection of multiple coefficients based on the provided information. For example, to select coefficients with \"Width\", specify keep=[r\"Width\"]:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4; keep=[r\"Width\"])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\nSepalWidth & PetalLength -0.070\n (0.041)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Regex works similarly to the exact string argument, so if the coefficients are labeled then the regex needs to match the relabeled value. It also expects any interaction to be based on & and categorical variables to be :, for example:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6)\n\n# output\n\n \n-------------------------------------------------\n SepalLength\n --------------------\n (1) (2)\n-------------------------------------------------\nSepalWidth 0.715*** 0.432***\n (0.156) (0.081)\nPetalLength 1.050*** 0.776***\n (0.144) (0.064)\nSepalWidth & PetalLength -0.085*\n (0.040)\n(Intercept) 2.390***\n (0.262)\nSpeciesDummy: versicolor -0.956***\n (0.215)\nSpeciesDummy: virginica -1.394***\n (0.286)\n-------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n-------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n-------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6; keep=[r\": \", r\" & \"])\n\n# output\n\n \n------------------------------------------------\n SepalLength\n -------------------\n (1) (2)\n------------------------------------------------\nSpeciesDummy: versicolor -0.956***\n (0.215)\nSpeciesDummy: virginica -1.394***\n (0.286)\nSepalWidth & PetalLength -0.085*\n (0.040)\n------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n------------------------------------------------\nControls Yes Yes\n------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n------------------------------------------------","category":"page"},{"location":"keep_drop_order/#By-Integer","page":"Keep Drop and Order Arguments","title":"By Integer","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Specifying an integer allows the selection of specific coefficients:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; drop=[3])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"In typical arrays, Julia specifies the end argument to access the last argument. This does not work without direct access to the array, so this package provides an :end symbol that is handled similarly:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1, :end, (:end, 2)])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nSepalLength 0.378***\n (0.066)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"The Tuple (:end, 2) is equivalent to end-2.","category":"page"},{"location":"keep_drop_order/#By-Range","page":"Keep Drop and Order Arguments","title":"By Range","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Specifying a range works similarly to an integer:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1:3])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"There is also a special symbol for selecting a range at the end, :last. By itself, :last works the same as :end, but in a Tuple (:last, x) selects the last x coefficients:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1, (:last, 2)])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/#Mixing-keep,-drop-and-order","page":"Keep Drop and Order Arguments","title":"Mixing keep, drop and order","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"As mentioned, keep is applied first, then drop and finally order. keep also will rearrange arguments, so integer and range arguments in order are applied to the resorted arguments from keep. It is also possible to mix the different types of selectors. For example, if you want the last coefficient and all coefficients that are interactions:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6; keep=[:end, r\" & \"])\n\n# output\n\n \n------------------------------------------------\n SepalLength\n -------------------\n (1) (2)\n------------------------------------------------\nSpeciesDummy: virginica -1.394***\n (0.286)\nSepalWidth & PetalLength -0.085*\n (0.040)\n------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n------------------------------------------------\nControls Yes Yes\n------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n------------------------------------------------","category":"page"},{"location":"keep_drop_order/#Relabeled-Coefficients","page":"Keep Drop and Order Arguments","title":"Relabeled Coefficients","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"By default, if labels change the name of the arguments, then keep, drop and order should use the relabeled versions of the coefficients. For example, if the coefficients are relabeled:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; labels=Dict(\"SepalWidth\" => \"SW\", \"PetalLength\" => \"PL\"), keep=[\"SW\", \"PL\"])\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength SW\n ------------------------------ -------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\nSW 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPL 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n---------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n---------------------------------------------------------------------\nControls Yes Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n---------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"To use the original coefficient names, set use_relabeled_values=false:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; labels=Dict(\"SepalWidth\" => \"SW\", \"PetalLength\" => \"PL\"), keep=[\"SepalWidth\", \"PetalLength\"], use_relabeled_values=false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength SW \n ------------------------------ -------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\nSW 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPL 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n---------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n---------------------------------------------------------------------\nControls Yes Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n---------------------------------------------------------------------","category":"page"},{"location":"function_reference/#Function-Reference","page":"Function and Type Reference","title":"Function Reference","text":"","category":"section"},{"location":"function_reference/","page":"Function and Type Reference","title":"Function and Type Reference","text":"Modules = [RegressionTables]","category":"page"},{"location":"examples/#Examples","page":"Examples","title":"Examples","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Pages=[\"examples.md\"]","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Setup for the following examples:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"DocTestSetup = quote # hide\n using RegressionTables, DataFrames, RDatasets, FixedEffectModels, GLM, MixedModels;\n df = RDatasets.dataset(\"datasets\", \"iris\");\n df[!,:isSmall] = df[!,:SepalWidth] .< 2.9;\n rr1 = reg(df, @formula(SepalLength ~ SepalWidth));\n rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)));\n rr3 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength * PetalWidth + fe(Species) + fe(isSmall)));\n rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)));\n rr5 = reg(df, @formula(SepalWidth ~ SepalLength + (PetalLength ~ PetalWidth) + fe(Species)));\n rr6 = glm(@formula(isSmall ~ PetalLength + PetalWidth + Species), df, Binomial());\n rr7 = glm(@formula(isSmall ~ SepalLength + PetalLength + PetalWidth), df, Binomial());\nend # hide","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"using RegressionTables, DataFrames, RDatasets, FixedEffectModels, GLM;\ndf = dataset(\"datasets\", \"iris\");\ndf[!,:isSmall] = df[!,:SepalWidth] .< 2.9;\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth));\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)));\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength * PetalWidth + fe(Species) + fe(isSmall)));\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)));\nrr5 = reg(df, @formula(SepalWidth ~ SepalLength + (PetalLength ~ PetalWidth) + fe(Species)));\nrr6 = glm(@formula(isSmall ~ PetalLength + PetalWidth + Species), df, Binomial());\nrr7 = glm(@formula(isSmall ~ SepalLength + PetalLength + PetalWidth), df, Binomial());","category":"page"},{"location":"examples/#Default","page":"Examples","title":"Default","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4,rr5,rr6)\n\n# output\n\n \n------------------------------------------------------------------------------------------\n SepalLength SepalWidth isSmall\n ------------------------------ ------------------ ---------\n (1) (2) (3) (4) (5) (6)\n------------------------------------------------------------------------------------------\n(Intercept) 6.526*** -1.917\n (0.479) (1.242)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188* 1.048** -0.773\n (0.064) (0.129) (0.083) (0.362) (0.554)\nPetalWidth -0.625 0.626*** -3.782**\n (0.354) (0.123) (1.256)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378*** -0.313\n (0.066) (0.239)\nSpecies: versicolor 10.441***\n (1.957)\nSpecies: virginica 13.230***\n (2.636)\n------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------------------------\nEstimator OLS OLS OLS OLS IV Binomial\n------------------------------------------------------------------------------------------\nN 150 150 150 150 150 150\nR2 0.014 0.863 0.868 0.635 0.080\nWithin-R2 0.642 0.598 0.391 -0.535\nFirst-stage F statistic 19.962\nPseudo R2 0.006 0.811 0.826 0.862 0.072 0.347\n------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Below-Statistics","page":"Examples","title":"Below Statistics","text":"","category":"section"},{"location":"examples/#StdError-(default)","page":"Examples","title":"StdError (default)","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = StdError)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#No-statistics","page":"Examples","title":"No statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = nothing)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\nSepalWidth -0.223 0.432*** 0.516***\nPetalLength 0.776*** 0.723*** -0.188*\nPetalWidth -0.625 0.626***\nPetalLength & PetalWidth 0.066\nSepalLength 0.378***\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#TStat","page":"Examples","title":"TStat","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = TStat)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (13.628)\nSepalWidth -0.223 0.432*** 0.516***\n (-1.440) (5.310) (4.982)\nPetalLength 0.776*** 0.723*** -0.188*\n (12.073) (5.615) (-2.246)\nPetalWidth -0.625 0.626***\n (-1.763) (5.072)\nPetalLength & PetalWidth 0.066\n (0.981)\nSepalLength 0.378***\n (5.761)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#ConfInt-(Confidence-Interval)","page":"Examples","title":"ConfInt (Confidence Interval)","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Confidence level defaults to the 95th percentile:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = ConfInt)\n\n# output\n\n \n------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n -------------------------------------------------- ----------------\n (1) (2) (3) (4)\n------------------------------------------------------------------------------------------------\n(Intercept) 6.526***\n (5.580, 7.473)\nSepalWidth -0.223 0.432*** 0.516***\n (-0.530, 0.083) (0.271, 0.593) (0.311, 0.721)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.649, 0.903) (0.469, 0.978) (-0.353, -0.023)\nPetalWidth -0.625 0.626***\n (-1.325, 0.076) (0.382, 0.870)\nPetalLength & PetalWidth 0.066\n (-0.067, 0.199)\nSepalLength 0.378***\n (0.248, 0.507)\n------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Set the Confidence Interval level either by setting RegressionTables.default_confint_level or by adjusting the confint_level keyword argument","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = ConfInt, confint_level=0.9, align=:c)\n\n# output\n\n \n-------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------------------------- ----------------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------------------------------\n(Intercept) 6.526***\n (5.734, 7.319)\nSepalWidth -0.223 0.432*** 0.516***\n (-0.480, 0.033) (0.297, 0.567) (0.345, 0.688)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.669, 0.882) (0.510, 0.937) (-0.326, -0.049)\nPetalWidth -0.625 0.626***\n (-1.211, -0.038) (0.421, 0.830)\nPetalLength & PetalWidth 0.066\n (-0.045, 0.177)\nSepalLength 0.378***\n (0.269, 0.486)\n-------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n-------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Standard-Errors-on-same-line-as-coefficient","page":"Examples","title":"Standard Errors on same line as coefficient","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; stat_below=false)\n\n# output\n\n \n----------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------------------------------ ----------------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------------------------------\n(Intercept) 6.526*** (0.479)\nSepalWidth -0.223 (0.155) 0.432*** (0.081) 0.516*** (0.104)\nPetalLength 0.776*** (0.064) 0.723*** (0.129) -0.188* (0.083)\nPetalWidth -0.625 (0.354) 0.626*** (0.123)\nPetalLength & PetalWidth 0.066 (0.067)\nSepalLength 0.378*** (0.066)\n----------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Keep,-drop-and-order","page":"Examples","title":"Keep, drop and order","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"See Keep Drop and Order Arguments","category":"page"},{"location":"examples/#Formatting-Estimates,-Statistics-and-decimal-points","page":"Examples","title":"Formatting Estimates, Statistics and decimal points","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Also see Customization of Defaults","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; estimformat = \"%02.5f\")\n\n# output\n\n \n----------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------\n(Intercept) 6.52622***\n (0.479)\nSepalWidth -0.22336 0.43222*** 0.51611***\n (0.155) (0.081) (0.104)\nPetalLength 0.77563*** 0.72335*** -0.18757*\n (0.064) (0.129) (0.083)\nPetalWidth -0.62469 0.62571***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.06596\n (0.067)\nSepalLength 0.37777***\n (0.066)\n----------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; digits = 4)\n\n# output\n\n \n-------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------- ----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.5262***\n (0.479)\nSepalWidth -0.2234 0.4322*** 0.5161***\n (0.155) (0.081) (0.104)\nPetalLength 0.7756*** 0.7234*** -0.1876*\n (0.064) (0.129) (0.083)\nPetalWidth -0.6247 0.6257***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.0660\n (0.067)\nSepalLength 0.3778***\n (0.066)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; statisticformat = \"%02.5f\")\n\n# output\n\n \n-------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------- ----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.47890)\nSepalWidth -0.223 0.432*** 0.516***\n (0.15508) (0.08139) (0.10359)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.06425) (0.12883) (0.08349)\nPetalWidth -0.625 0.626***\n (0.35439) (0.12338)\nPetalLength & PetalWidth 0.066\n (0.06726)\nSepalLength 0.378***\n (0.06557)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.01382 0.86331 0.86824 0.63516\nWithin-R2 0.64151 0.59784 0.39114\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; digits_stats = 4)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.0138 0.8633 0.8682 0.6352\nWithin-R2 0.6415 0.5978 0.3911\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#Labeling-Coefficients","page":"Examples","title":"Labeling Coefficients","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"labels is applied first, transform_labels applies to within each coefficient","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2; labels = Dict(\n \"SepalLength\" => \"My dependent variable: SepalLength\",\n \"PetalLength\" => \"Length of Petal\",\n \"PetalWidth\" => \"Width of Petal\",\n \"(Intercept)\" => \"Const.\" ,\n \"isSmall\" => \"isSmall Dummies\",\n \"SpeciesDummy\" => \"Species Dummies\"\n))\n\n# output\n\n \n-----------------------------------------------------------\n My dependent variable: SepalLength\n -----------------------------------\n (1) (2)\n-----------------------------------------------------------\nConst. 6.526***\n (0.479)\nSepalWidth -0.223 0.432***\n (0.155) (0.081)\nLength of Petal 0.776***\n (0.064)\n-----------------------------------------------------------\nSpecies Fixed Effects Yes\n-----------------------------------------------------------\nN 150 150\nR2 0.014 0.863\nWithin-R2 0.642\n-----------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Each piece of an interaction term (or categorical term) is labeled based on its components:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr3; labels=Dict(\n \"SepalWidth\" => \"Sepal Width\",\n \"PetalLength\" => \"Petal Length\",\n \"PetalWidth\" => \"Petal Width\"\n)) # it is not necessary to specify a \"PetalLength & PetalWidth\" label\n\n# output\n\n \n----------------------------------------\n SepalLength\n----------------------------------------\nSepal Width 0.516***\n (0.104)\nPetal Length 0.723***\n (0.129)\nPetal Width -0.625\n (0.354)\nPetal Length & Petal Width 0.066\n (0.067)\n----------------------------------------\nSpecies Fixed Effects Yes\nisSmall Fixed Effects Yes\n----------------------------------------\nN 150\nR2 0.868\nWithin-R2 0.598\n----------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"transform_labels uses the replace function, so the name does not have to match completely:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr2, rr3; transform_labels = Dict(\"Width\" => \" Width\", \"Length\" => \" Length\"))\n\n# output\n\n \n-----------------------------------------------------------\n Sepal Length\n ------------------------------\n (1) (2) (3)\n-----------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepal Width -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetal Length 0.776*** 0.723***\n (0.064) (0.129)\nPetal Width -0.625\n (0.354)\nPetal Length & Petal Width 0.066\n (0.067)\n-----------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n-----------------------------------------------------------\nN 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\n-----------------------------------------------------------","category":"page"},{"location":"examples/#Grouping-Regressions","page":"Examples","title":"Grouping Regressions","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Groups are placed above the dependent variable names, allowing you to specify splits or some other group information. Repeated group names are automatically combined.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"grp1\", \"grp1\", \"grp2\", \"grp2\"])\n\n# output\n\n \n-------------------------------------------------------------------------\n grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"If the length of groups is one more than the number of regressions, the first element is placed in the column above the coefficient names:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"My Group:\", \"grp1\", \"grp1\", \"grp2\", \"grp2\"])\n\n# output\n\n \n-------------------------------------------------------------------------\nMy Group: grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can also specify groups with integer ranges, just note that column 1 is the column with the coefficient names:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"My Group:\", \"grp1\" => 2:3, \"grp2\" => 4:5])\n\n# output\n\n \n-------------------------------------------------------------------------\nMy Group: grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/#Multi-Level-Groups","page":"Examples","title":"Multi-Level Groups","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Specify multiple group levels by passing a matrix or a vector of vectors:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\n \"grp parent\" \"grp parent\" \"grp parent\" \"other group\";\n \"grp1\" \"grp1\" \"grp2\" \"grp2\"\n])\n\n# output\n\n \n-------------------------------------------------------------------------\n grp parent other group\n -------------------------------- -----------\n grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\n [\"Parent Group:\", \"grp parent\" => 2:4, \"other group\"],\n [\"grp1\", \"grp1\", \"grp2\", \"grp2\"]\n])\n\n# output\n\n \n----------------------------------------------------------------------------\nParent Group: grp parent other group\n ----------------------------------- -----------\n grp1 grp2\n ---------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ---------------------- ---------- -----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n----------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n----------------------------------------------------------------------------","category":"page"},{"location":"examples/#Do-not-print-X-block","page":"Examples","title":"Do not print $X block","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_fe_section = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_depvar = false)\n\n# output\n\n \n---------------------------------------------------------------------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_estimator_section = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; number_regressions = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/#Re-order-Fixed-Effects","page":"Examples","title":"Re-order Fixed Effects","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Similar arguments to Keep Drop and Order Arguments (equivalent to keep before the fe_suffix is applied)","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; fixedeffects = [r\"isSmall\", \"SpeciesDummy\"])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#Change-Labels-for-Regression-Statistics","page":"Examples","title":"Change Labels for Regression Statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Also see Customization of Defaults","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; regression_statistics=[\n Nobs => \"Number of Observations\",\n R2,\n AdjR2 => \"Adj. R2\"\n])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nNumber of Observations 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nAdj. R2 0.007 0.860 0.861 0.622\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#All-Available-Statistics","page":"Examples","title":"All Available Statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr5; regression_statistics = [\n Nobs,\n R2,\n PseudoR2,\n R2CoxSnell,\n R2Nagelkerke,\n R2Deviance,\n AdjR2,\n AdjPseudoR2,\n AdjR2Deviance,\n DOF,\n LogLikelihood,\n AIC,\n AICC,\n BIC,\n FStat,\n FStatPValue,\n FStatIV,\n FStatIVPValue,\n R2Within\n])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 1.048**\n (0.064) (0.129) (0.362)\nPetalWidth -0.625\n (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -0.313\n (0.239)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nEstimator OLS OLS OLS IV\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.080\nPseudo R2 0.006 0.811 0.826 0.072\nCox-Snell R2 0.014 0.863 0.868 0.080\nNagelkerke R2 0.015 0.944 0.950 0.116\nDeviance R2 0.014 0.863 0.868 0.080\nAdjusted R2 0.007 0.860 0.861 0.055\nPseudo Adjusted R2 0.000 0.789 0.782 0.026\nDeviance Adjusted R2 0.007 0.860 0.861 0.055\nDegrees of Freedom 147 145 141 145\nLog Likelihood -182.996 -34.787 -32.031 -81.497\nAIC 367.992 73.575 72.062 166.995\nAICC 368.019 73.657 72.338 167.076\nBIC 368.019 73.657 72.338 167.076\nF 2.074 129.736 52.402 17.468\nF-test p value 0.152 0.000 0.000 0.000\nFirst-stage F statistic 19.962\nFirst-stage p value 0.000\nWithin-R2 0.642 0.598 -0.535\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#LaTeX-Output","page":"Examples","title":"LaTeX Output","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable())\n\n# output\n\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\\n\\midrule\n(Intercept) & 6.526*** & & & \\\\\n & (0.479) & & & \\\\\nSepalWidth & -0.223 & 0.432*** & 0.516*** & \\\\\n & (0.155) & (0.081) & (0.104) & \\\\\nPetalLength & & 0.776*** & 0.723*** & -0.188* \\\\\n & & (0.064) & (0.129) & (0.083) \\\\\nPetalWidth & & & -0.625 & 0.626*** \\\\\n & & & (0.354) & (0.123) \\\\\nPetalLength $\\times$ PetalWidth & & & 0.066 & \\\\\n & & & (0.067) & \\\\\nSepalLength & & & & 0.378*** \\\\\n & & & & (0.066) \\\\\n\\midrule\nSpecies Fixed Effects & & Yes & Yes & Yes \\\\\nisSmall Fixed Effects & & & Yes & \\\\\n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\\n$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\\\\nWithin-$R^2$ & & 0.642 & 0.598 & 0.391 \\\\\n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"examples/#Extralines","page":"Examples","title":"Extralines","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Extralines are added to the end of a regression table","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4;\n extralines=[\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"]\n)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nSpecification: Option 1 Option 2 Option 3 Option 4\n----------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can specify that a single value should fill two columns, note that these will inherit the alignment from their section (so with the default align=:r, the below example would have items below the second and fourth regression):","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; extralines=[\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n [\"Difference in coefficients\", 1.503 => 2:3, 3.515 => 4:5]\n], align=:c)\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nSpecification: Option 1 Option 2 Option 3 Option 4\nDifference in coefficients 1.503 3.515\n------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can use the DataRow function to allow for more control, such as underlines and alignment","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; extralines=[\n DataRow([\"Difference in coefficients\", 1.5032 => 2:3, 3.5152 => 4:5]; align = \"lcc\", print_underlines=[false, true, true]),\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nDifference in coefficients 1.503 3.515\n ------------------- ---------------------\nSpecification: Option 1 Option 2 Option 3 Option 4\n------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Works similarly with HTML or Latex:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render=LatexTable(), extralines=[\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n DataRow([\"Difference in coefficients\", 1.503 => 2:3, 3.515 => 4:5]; align = \"lcc\", print_underlines=[false, true, true])\n]) # use DataRow to customize alignment\n\n# output\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\\n\\midrule\n(Intercept) & 6.526*** & & & \\\\\n & (0.479) & & & \\\\\nSepalWidth & -0.223 & 0.432*** & 0.516*** & \\\\\n & (0.155) & (0.081) & (0.104) & \\\\\nPetalLength & & 0.776*** & 0.723*** & -0.188* \\\\\n & & (0.064) & (0.129) & (0.083) \\\\\nPetalWidth & & & -0.625 & 0.626*** \\\\\n & & & (0.354) & (0.123) \\\\\nPetalLength $\\times$ PetalWidth & & & 0.066 & \\\\\n & & & (0.067) & \\\\\nSepalLength & & & & 0.378*** \\\\\n & & & & (0.066) \\\\\n\\midrule\nSpecies Fixed Effects & & Yes & Yes & Yes \\\\\nisSmall Fixed Effects & & & Yes & \\\\\n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\\n$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\\\\nWithin-$R^2$ & & 0.642 & 0.598 & 0.391 \\\\\nSpecification: & Option 1 & Option 2 & Option 3 & Option 4 \\\\\nDifference in coefficients & \\multicolumn{2}{c}{1.503} & \\multicolumn{2}{c}{3.515} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"examples/#Do-Not-Print-Fixed-Effect-Suffix","page":"Examples","title":"Do Not Print Fixed Effect Suffix","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr2, rr3, rr7; print_fe_suffix=false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Yes Yes\nisSmall Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/#Standardize-Coefficients","page":"Examples","title":"Standardize Coefficients","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Standardizing coefficients adjusts each coefficient by its standard deviation and the standard deviation of the Y variable, making the coefficients equivalent to a 1 standard deviation in X leads to a (result) standard deviation change in Y. This is only possible for regressions that store enough information to calculate these standard deviations, currently GLM.jl and MixedModels.jl. The intercept, lacking a standard deviation, is simply the number of standard deviations of Y.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"lm1 = lm(@formula(SepalLength ~ SepalWidth), df);\nlm2 = lm(@formula(SepalLength ~ SepalWidth + PetalLength + Species), df);\nregtable(lm1, lm2, rr6, rr7; standardize_coef=true)\n\n# output\n\n \n------------------------------------------------------------------\n SepalLength isSmall\n -------------------- ---------------------\n (1) (2) (3) (4)\n------------------------------------------------------------------\n(Intercept) 7.881*** 2.887*** -4.119 21.894***\n (0.578) (0.317) (2.669) (5.601)\nSepalWidth -0.118 0.228***\n (0.082) (0.043)\nPetalLength 1.654*** -2.934 13.578***\n (0.137) (2.102) (2.686)\nSpecies: versicolor -0.546*** 10.611***\n (0.123) (1.989)\nSpecies: virginica -0.796*** 13.445***\n (0.163) (2.679)\nPetalWidth -6.193** -5.957**\n (2.057) (1.846)\nSepalLength -6.260***\n (1.240)\n------------------------------------------------------------------\nEstimator OLS OLS Binomial Binomial\n------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863\nPseudo R2 0.006 0.811 0.347 0.297\n------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"It is also possible to standardize some coefficients and not others","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"lm1 = lm(@formula(SepalLength ~ SepalWidth), df);\nregtable(lm1, lm1, rr7, rr7; standardize_coef=[false, true, false, true])\n\n# output\n\n \n---------------------------------------------------------\n SepalLength isSmall\n ------------------- ---------------------\n (1) (2) (3) (4)\n---------------------------------------------------------\n(Intercept) 6.526*** 7.881*** 10.189*** 21.894***\n (0.479) (0.578) (2.607) (5.601)\nSepalWidth -0.223 -0.118\n (0.155) (0.082)\nSepalLength -3.519*** -6.260***\n (0.697) (1.240)\nPetalLength 3.580*** 13.578***\n (0.708) (2.686)\nPetalWidth -3.637** -5.957**\n (1.127) (1.846)\n---------------------------------------------------------\nEstimator OLS OLS Binomial Binomial\n---------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.014\nPseudo R2 0.006 0.006 0.297 0.297\n---------------------------------------------------------","category":"page"},{"location":"examples/#Show-Clustered-Standard-Errors","page":"Examples","title":"Show Clustered Standard Errors","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Displays whether or not the standard errors are clustered and in what ways.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"df_cigar = RDatasets.dataset(\"plm\", \"Cigar\");\n\nrr_c1 = reg(df_cigar, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State));\nrr_c2 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State) + fe(Year)), Vcov.cluster(:State, :Year));\nrr_c3 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State)), Vcov.cluster(:Year));\nregtable(rr_c1, rr_c2, rr_c3; print_clusters=true, labels=Dict(\"Year\" => \"Sales Year\"))\n\n# output\n\n \n----------------------------------------------------------\n Sales\n -------------------------------\n (1) (2) (3)\n----------------------------------------------------------\nNDI -0.007** -0.005* 0.002*\n (0.003) (0.003) (0.001)\nPrice -0.823** -0.413***\n (0.230) (0.084)\n----------------------------------------------------------\nState Fixed Effects Yes Yes Yes\nSales Year Fixed Effects Yes Yes\n----------------------------------------------------------\nState Clustering Yes Yes\nSales Year Clustering Yes Yes\n----------------------------------------------------------\nN 1,380 1,380 1,380\nR2 0.832 0.846 0.774\nWithin-R2 0.154 0.227 0.273\n----------------------------------------------------------","category":"page"},{"location":"examples/#MixedModels-Support","page":"Examples","title":"MixedModels Support","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"This package does support MixedModels.jl, but instead of displaying fixed effects it will display the variation from the random effects.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"form1 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + load | item) +\n (1 + spkr + prec + load | subj))\ncontr = Dict(:spkr => EffectsCoding(),\n :prec => EffectsCoding(),\n :load => EffectsCoding(),\n :item => Grouping(),\n :subj => Grouping())\n# to make sure the results are always the same, these values help fix the model into one result\nfmreθ = [\n 0.8648075226444749, 0.43344406279292136, 0.532698219245229,\n 0.03139575786126669, 0.269825335511795, 0.5307313041693793,\n 0.23438217856147925, 0.0349964462168697, 0.948766814931185,\n 0.40866263683286375, 0.6055999220729944, 0.9928229644500718,\n 0.05342261972167761,\n]\nkbm1 = updateL!(setθ!(LinearMixedModel(form1, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nform2 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + spkr + prec + load | subj))\n\nfmreθ = [\n 0.27115451643185495, 0.02114691520013967, 0.8794734878503344,\n 0.7343424423913391, 0.6603201740011742, 0.8497808579576883,\n 0.6355311618411573, 0.7807843933484198, 0.9669197738773895,\n 0.03814101806846881,\n]\nkbm2 = updateL!(setθ!(LinearMixedModel(form2, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nform3 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + load | item) +\n (1 + spkr + prec * load | subj))\n\nfmreθ = [\n 0.7221403658923715, 0.3078425012729602, 0.2917886795704724,\n 0.5000142926713435, 0.7426865162047754, 0.1731367021580622,\n 0.020327890985133656, 0.7595447732279332, 0.48724482279872006,\n 0.6205745741154292, 0.3954285463498247, 0.09594315730251379,\n 0.13946651488431383, 0.6672989094861689, 0.2341117878022333,\n 0.053650218835408436, 0.143772505670828, 0.027822254707002392,\n]\nkbm3 = updateL!(setθ!(LinearMixedModel(form3, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nregtable(kbm1, kbm2, kbm3; labels=Dict(\n \"subj\" => \"Subject\",\n \"item\" => \"Item\",\n \"load: yes\" => \"Load\",\n \"prec: maintain\" => \"Prec\",\n \"spkr: old\" => \"Old Speaker\"\n )\n)\n\n# output\n\n \n---------------------------------------------------------------\n rt_trunc\n ---------------------------------------\n (1) (2) (3)\n---------------------------------------------------------------\n(Intercept) 2181.911*** 2182.017*** 2180.342***\n (117.772) (35.214) (58.616)\nOld Speaker 68.034 67.884 67.225*\n (53.386) (74.384) (28.793)\nPrec -333.636*** -333.785* -334.362***\n (76.398) (158.971) (66.565)\nLoad 78.532 78.426 75.764\n (167.790) (150.341) (70.860)\n---------------------------------------------------------------\nItem | (Intercept) 447.735 40.270\nItem | Load 735.081 109.918\nSubject | (Intercept) 639.374 220.685 542.038\nSubject | Old Speaker 377.476 537.692 265.104\nSubject | Prec 556.544 1,180.880 514.950\nSubject | Load 783.505 1,115.791 751.626\nSubject | Prec & Load 855.252\n---------------------------------------------------------------\nN 1,789 1,789 1,789\nLog Likelihood -14,685.198 -14,765.033 -14,711.866\n---------------------------------------------------------------","category":"page"},{"location":"examples/#Typst-Support","page":"Examples","title":"Typst Support","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Similar to Latex, this package can produce Typst Tables. This requires Typst v0.11.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render = TypstTable())\n\n# output\n\n#table( \n columns: (auto, auto, auto, auto, auto),\n align: (left, right, right, right, right),\n column-gutter: 1fr,\n stroke: none,\n\n table.hline(), \n [] , table.cell(colspan: 3, align: center)[SepalLength] , table.cell(colspan: 1, align: center)[SepalWidth],\n table.hline(start: 1, end: 4, stroke: 0.5pt), table.hline(start: 4, end: 5, stroke: 0.5pt),\n [] , [(1)], [(2)], [(3)], [(4)],\n table.hline(stroke: 0.7pt),\n [(Intercept)] , [6.526$\"\"^(***)$], [], [], [],\n [] , [(0.479)], [], [], [],\n [SepalWidth] , [-0.223], [0.432$\"\"^(***)$], [0.516$\"\"^(***)$], [],\n [] , [(0.155)], [(0.081)], [(0.104)], [],\n [PetalLength] , [], [0.776$\"\"^(***)$], [0.723$\"\"^(***)$], [-0.188$\"\"^(*)$],\n [] , [], [(0.064)], [(0.129)], [(0.083)],\n [PetalWidth] , [], [], [-0.625], [0.626$\"\"^(***)$],\n [] , [], [], [(0.354)], [(0.123)],\n [PetalLength $times$ PetalWidth], [], [], [0.066], [],\n [] , [], [], [(0.067)], [],\n [SepalLength] , [], [], [], [0.378$\"\"^(***)$],\n [] , [], [], [], [(0.066)],\n table.hline(stroke: 0.7pt),\n [Species Fixed Effects] , [], [Yes], [Yes], [Yes],\n [isSmall Fixed Effects] , [], [], [Yes], [],\n table.hline(stroke: 0.7pt),\n [_N_] , [150], [150], [150], [150],\n [_R_$\"\"^2$] , [0.014], [0.863], [0.868], [0.635],\n [Within-_R_$\"\"^2$] , [], [0.642], [0.598], [0.391],\n table.hline(),\n)","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr6, rr7; render = TypstTable())\n\n# output\n\n#table( \n columns: (auto, auto, auto, auto),\n align: (left, right, right, right),\n column-gutter: 1fr,\n stroke: none,\n\n table.hline(),\n [] , table.cell(colspan: 1, align: center)[SepalLength], table.cell(colspan: 2, align: center)[isSmall],\n table.hline(start: 1, end: 2, stroke: 0.5pt), table.hline(start: 2, end: 4, stroke: 0.5pt),\n [] , [(1)], [(2)], [(3)],\n table.hline(stroke: 0.7pt),\n [(Intercept)] , [6.526$\"\"^(***)$], [-1.917], [10.189$\"\"^(***)$],\n [] , [(0.479)], [(1.242)], [(2.607)],\n [SepalWidth] , [-0.223], [], [],\n [] , [(0.155)], [], [],\n [PetalLength] , [], [-0.773], [3.580$\"\"^(***)$],\n [] , [], [(0.554)], [(0.708)],\n [PetalWidth] , [], [-3.782$\"\"^(**)$], [-3.637$\"\"^(**)$],\n [] , [], [(1.256)], [(1.127)],\n [Species: versicolor], [], [10.441$\"\"^(***)$], [],\n [] , [], [(1.957)], [],\n [Species: virginica] , [], [13.230$\"\"^(***)$], [],\n [] , [], [(2.636)], [],\n [SepalLength] , [], [], [-3.519$\"\"^(***)$],\n [] , [], [], [(0.697)],\n table.hline(stroke: 0.7pt),\n [Estimator] , [OLS], [Binomial], [Binomial],\n table.hline(stroke: 0.7pt),\n [_N_] , [150], [150], [150],\n [_R_$\"\"^2$] , [0.014], [], [],\n [Pseudo _R_$\"\"^2$] , [0.006], [0.347], [0.297],\n table.hline(),\n)","category":"page"},{"location":"regression_statistics/#Regression-Statistics","page":"Regression Statistics","title":"Regression Statistics","text":"","category":"section"},{"location":"regression_statistics/#Full-Regression-Statistics","page":"Regression Statistics","title":"Full Regression Statistics","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.AbstractRegressionStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AbstractRegressionStatistic","page":"Regression Statistics","title":"RegressionTables.AbstractRegressionStatistic","text":"AbstractRegressionStatistic encapsulates all regression statistics (e.g., number of observations, R^2, etc.). In most cases, the individual regression packages provide functions that access these, generally from the StatsAPI.jl package. If the function does not exist in the regression package, it is typically added in the extension to this package. Since some statistics are not relevant for all regressions, the value of the statistic is wrapped in a Union with Nothing to indicate that the value is not available.\n\nTo define a new regression statistic, three things are needed:\n\nA new type that is a subtype of AbstractRegressionStatistic\nA constructor that takes a RegressionModel and returns the new type (or nothing if the statistic is not available)\nA label function that is dependent on the AbstractRenderType and the type provided. This label is what is displayed in the left most column of the regression table.\n\nIt is also helpful to maintain consistency by defining the value as val within the struct.\n\nFor example:\n\nstruct YMean <: RegressionTable.AbstractRegressionStatistic\n val::Union{Float64, Nothing}\nend\nYMean(x::RegressionModel) = try\n YMean(mean(x.model.rr.y))\ncatch\n YMean(nothing)\nend\nRegressionTable.label(render::AbstractRenderType, x::Type{YMean}) = \"Mean of Y\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Defined-Types","page":"Regression Statistics","title":"Defined Types","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractRegressionStatistic && t != RegressionTables.AbstractRegressionStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AIC","page":"Regression Statistics","title":"RegressionTables.AIC","text":"AIC is the Akaike Information Criterion of the regression. Labels default to \"AIC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AICC","page":"Regression Statistics","title":"RegressionTables.AICC","text":"AICC is the Corrected Akaike Information Criterion of the regression. Labels default to \"AICC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AbstractR2","page":"Regression Statistics","title":"RegressionTables.AbstractR2","text":"abstract type AbstractR2 <: AbstractRegressionStatistic end\n\nParent type for all R^2 statistics. This is available to change the formatting of all R^2 statistics. For example, if the desired display for R^2 is in the percentage term, run:\n\nBase.repr(render::AbstractRenderType, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * \"%\"\n# add second definition since Latex needs % escaped\nBase.repr(render::AbstractRenderType::RegressionTables.AbstractLatex, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * \"\\%\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjPseudoR2","page":"Regression Statistics","title":"RegressionTables.AdjPseudoR2","text":"See AdjR2McFadden for details.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2","page":"Regression Statistics","title":"RegressionTables.AdjR2","text":"AdjR2 is the Adjusted R^2 of the regression. Labels default to:\n\n\"Adjusted R2\" for AbstractAscii\n\"Adjusted R^2\" for AbstractLatex\n\"Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2Deviance","page":"Regression Statistics","title":"RegressionTables.AdjR2Deviance","text":"AdjR2Deviance is the Deviance Adjusted R^2 of the regression. Labels default to:\n\n\"Deviance Adjusted R2\" for AbstractAscii\n\"Deviance Adjusted R^2\" for AbstractLatex\n\"Deviance Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2McFadden","page":"Regression Statistics","title":"RegressionTables.AdjR2McFadden","text":"AdjR2McFadden is the McFadden Adjusted R^2 of the regression (often referred to as the Pseudo Adjusted R^2). Labels default to:\n\n\"Pseudo Adjusted R2\" for AbstractAscii\n\"Pseudo Adjusted R^2\" for AbstractLatex\n\"Pseudo Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.BIC","page":"Regression Statistics","title":"RegressionTables.BIC","text":"BIC is the Bayesian Information Criterion of the regression. Labels default to \"BIC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.DOF","page":"Regression Statistics","title":"RegressionTables.DOF","text":"DOF is the remaining degrees of freedom in the regression. Labels default to \"Degrees of Freedom\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStat","page":"Regression Statistics","title":"RegressionTables.FStat","text":"FStat is the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"F\" for AbstractAscii\n\"F\" for AbstractLatex\n\"F\" for AbstractHtml\n\nnote: Note\nthe FStat label is used in other labels, so changing it will change those labels as well.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatIV","page":"Regression Statistics","title":"RegressionTables.FStatIV","text":"FStatIV is the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"First-stage F statistic\" for AbstractAscii\n\"First-stage F statistic\" for AbstractLatex\n\"First-stage F statistic\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatIVPValue","page":"Regression Statistics","title":"RegressionTables.FStatIVPValue","text":"FStatIVPValue is the p-value of the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"First-stage p value\" for AbstractAscii\n\"First-stage p value\" for AbstractLatex\n\"First-stage p value\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatPValue","page":"Regression Statistics","title":"RegressionTables.FStatPValue","text":"FStatPValue is the p-value of the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"F p value\" for AbstractAscii\n\"F p value\" for AbstractLatex\n\"F p value\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.LogLikelihood","page":"Regression Statistics","title":"RegressionTables.LogLikelihood","text":"LogLikelihood is the log likelihood of the regression. Labels default to \"Log Likelihood\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.Nobs","page":"Regression Statistics","title":"RegressionTables.Nobs","text":"Nobs is the number of observations in the regression. Labels default to:\n\n\"N\" for AbstractAscii\n\"N\" for AbstractLatex\n\"N\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.PseudoR2","page":"Regression Statistics","title":"RegressionTables.PseudoR2","text":"See R2McFadden for details.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2","page":"Regression Statistics","title":"RegressionTables.R2","text":"R2 is the R^2 of the regression. Labels default to:\n\n\"R2\" for AbstractAscii\n\"R^2\" for AbstractLatex\n\"R2\" for AbstractHtml\n\nnote: Note\nThe label for R2 is used in other related statistics. So changing the label for R2 will change the label for other R^2 statistics as well.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2CoxSnell","page":"Regression Statistics","title":"RegressionTables.R2CoxSnell","text":"R2CoxSnell is the Cox-Snell R^2 of the regression. Labels default to:\n\n\"Cox-Snell R2\" for AbstractAscii\n\"Cox-Snell R^2\" for AbstractLatex\n\"Cox-Snell R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Deviance","page":"Regression Statistics","title":"RegressionTables.R2Deviance","text":"R2Deviance is the Deviance R^2 of the regression. Labels default to:\n\n\"Deviance R2\" for AbstractAscii\n\"Deviance R^2\" for AbstractLatex\n\"Deviance R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2McFadden","page":"Regression Statistics","title":"RegressionTables.R2McFadden","text":"R2McFadden is the McFadden R^2 of the regression (often referred to as the Pseudo-R^2). Labels default to:\n\n\"Pseudo R2\" for AbstractAscii\n\"Pseudo R^2\" for AbstractLatex\n\"Pseudo R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Nagelkerke","page":"Regression Statistics","title":"RegressionTables.R2Nagelkerke","text":"R2Nagelkerke is the Nagelkerke R^2 of the regression. Labels default to:\n\n\"Nagelkerke R2\" for AbstractAscii\n\"Nagelkerke R^2\" for AbstractLatex\n\"Nagelkerke R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Within","page":"Regression Statistics","title":"RegressionTables.R2Within","text":"R2Within is the within R-squared of a fixed effects regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"Within R2\" for AbstractAscii\n\"Within R^2\" for AbstractLatex\n\"Within R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Below-or-Under-Statistic","page":"Regression Statistics","title":"Below or Under Statistic","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.AbstractUnderStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AbstractUnderStatistic","page":"Regression Statistics","title":"RegressionTables.AbstractUnderStatistic","text":"abstract type AbstractUnderStatistic end\n\nThe abstract type for statistics that are below or next to the coefficients (e.g., standard errors, t-statistics, confidence intervals, etc.). The default available values are:\n\nStdError\nTStat\nConfInt\n\nNew values can be added by subtyping AbstractUnderStatistic and defining the struct and a constructor. The constructer should accept the standard error, the coefficient, and the degrees of freedom.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractUnderStatistic && t != RegressionTables.AbstractUnderStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.ConfInt","page":"Regression Statistics","title":"RegressionTables.ConfInt","text":"struct ConfInt <: AbstractUnderStatistic\n val::Tuple{Float64, Float64}\nend\nConfInt(rr::RegressionModel, k::Int; level=0.95, standardize=false, vargs...)\n\nThe confidence interval of a coefficient. The default confidence level is 95% (can be changed by setting RegressionTable.default_confint_level(render::AbstractRenderType, rr) = 0.90 or similar).\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.StdError","page":"Regression Statistics","title":"RegressionTables.StdError","text":"struct StdError <: AbstractUnderStatistic\n val::Float64\nend\nStdError(rr::RegressionModel, k::Int; standardize=false, vargs...)\n\nThe standard error of a coefficient.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.TStat","page":"Regression Statistics","title":"RegressionTables.TStat","text":"struct TStat <: AbstractUnderStatistic\n val::Float64\nend\nTStat(rr::RegressionModel, k::Int; vargs...)\n\nThe t-statistic of a coefficient.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Other-Statistics","page":"Regression Statistics","title":"Other Statistics","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.CoefValue\nRegressionTables.HasControls\nRegressionTables.RegressionNumbers\nRegressionTables.FixedEffectValue\nRegressionTables.RandomEffectValue\nRegressionTables.ClusterValue","category":"page"},{"location":"regression_statistics/#RegressionTables.CoefValue","page":"Regression Statistics","title":"RegressionTables.CoefValue","text":"struct CoefValue\n val::Float64\n pvalue::Float64\nend\n\nThe value of a coefficient and its p-value.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.HasControls","page":"Regression Statistics","title":"RegressionTables.HasControls","text":"struct HasControls\n val::Bool\nend\n\nIndicates whether the regression has coefficients left out of the table. HasControls is used as a label, which defaults to \"Controls\". This can be changed by setting\n\nRegressionTables.label(render::AbstractRenderType, x::Type{RegressionTables.HasControls}) = $name\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.RegressionNumbers","page":"Regression Statistics","title":"RegressionTables.RegressionNumbers","text":"struct RegressionNumbers\n val::Int\nend\n\nUsed to define which column number the regression is in. Primarily, this is used to control how these values are displayed. The default displays these as ($i), which can be set by running\n\nRegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FixedEffectValue","page":"Regression Statistics","title":"RegressionTables.FixedEffectValue","text":"struct FixedEffectValue\n val::Bool\nend\n\nA simple store of true/false for whether a fixed effect is used in the regression, used to determine how to display the value. The default is \"Yes\" and \"\", which can be changed by setting fe_value.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.RandomEffectValue","page":"Regression Statistics","title":"RegressionTables.RandomEffectValue","text":"struct RandomEffectValue\n val::Real\nend\n\nA simple sotre of the random effect value, by default equal to the standard deviation of the random effect. Typically will then be displayed the same as other Float64 values.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.ClusterValue","page":"Regression Statistics","title":"RegressionTables.ClusterValue","text":"struct ClusterValue\n val::Int\nend\n\nA simple store of the number of clusters used in the regression. Typically will be displayed the same as other Bool values (e.g., \"Yes\" or \"\").\n\n\n\n\n\n","category":"type"},{"location":"#RegressionTables.jl","page":"Introduction","title":"RegressionTables.jl","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"This package provides publication-quality regression tables for use with FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl, as well as any package that implements the RegressionModel abstraction.","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"In its objective it is similar to (and heavily inspired by) the Stata command esttab and the R package stargazer.","category":"page"},{"location":"#Table-of-Contents","page":"Introduction","title":"Table of Contents","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"","category":"page"},{"location":"#Installation","page":"Introduction","title":"Installation","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"To install the package, type in the Julia command prompt","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"] add RegressionTables","category":"page"},{"location":"#A-brief-demonstration","page":"Introduction","title":"A brief demonstration","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"using RegressionTables, DataFrames, FixedEffectModels, RDatasets, GLM\n\ndf = RDatasets.dataset(\"datasets\", \"iris\")\n\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(Species)))\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)))\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(Species)))\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)))\nrr5 = glm(@formula(SepalWidth < 2.9 ~ PetalLength + PetalWidth + Species), df, Binomial())\n\nregtable(\n rr1,rr2,rr3,rr4,rr5;\n render = AsciiTable(),\n labels = Dict(\n \"versicolor\" => \"Versicolor\",\n \"virginica\" => \"Virginica\",\n \"PetalLength\" => \"Petal Length\",\n ),\n regression_statistics = [\n Nobs => \"Obs.\",\n R2,\n R2Within,\n PseudoR2 => \"Pseudo-R2\",\n ],\n extralines = [\n [\"Main Coefficient\", \"SepalWidth\", \"SepalWidth\", \"Petal Length\", \"Petal Length\", \"Intercept\"],\n DataRow([\"Coef Diff\", 0.372 => 2:3, 1.235 => 3:4, \"\"], align=\"lccr\")\n ],\n order = [r\"Int\", r\" & \", r\": \"]\n)","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"LaTeX output can be generated by using","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable())","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Similarly, HTML tables can be created with HtmlTable().","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Send the output to a text file by passing the destination file as a keyword argument:","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable(), file=\"myoutputfile.tex\")","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"then use \\input in LaTeX to include that file in your code. Be sure to use the booktabs package:","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"\\documentclass{article}\n\\usepackage{booktabs}\n\n\\begin{document}\n\n\\begin{table}\n\\label{tab:mytable}\n\\input{myoutputfile}\n\\end{table}\n\n\\end{document}","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable() can also print TableRegressionModel's from GLM.jl (and output from other packages that produce TableRegressionModel's):","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"using GLM\n\ndobson = DataFrame(Counts = [18.,17,15,20,10,20,25,13,12],\n Outcome = repeat([\"A\", \"B\", \"C\"], outer = 3),\n Treatment = repeat([\"a\",\"b\", \"c\"], inner = 3))\nrr1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)\nlm1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)\ngm1 = fit(GeneralizedLinearModel, @formula(Counts ~ 1 + Outcome + Treatment), dobson,\n Poisson())\n\nregtable(rr1,lm1,gm1; renderSettings = AsciiTable())","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Printing of StatsBase.RegressionModels (e.g., MixedModels.jl and GLFixedEffectModels.jl) generally works but are less well tested; please file as issue if you encounter problems printing them.","category":"page"},{"location":"#Main-regtable-Function","page":"Introduction","title":"Main regtable Function","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"RegressionTables.regtable","category":"page"},{"location":"#RegressionTables.regtable","page":"Introduction","title":"RegressionTables.regtable","text":"Produces a publication-quality regression table, similar to Stata's esttab and R's stargazer.\n\nArguments\n\nrr::FixedEffectModel... are the FixedEffectModels from FixedEffectModels.jl that should be printed. Only required argument.\nkeep is a Vector of regressor names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all regressors will be shown. The strings should be the relabeld names of the coefficients.\ndrop is a Vector of regressor names (Strings), integers, ranges or regex that should not be shown. Defaults to an empty vector, in which case no regressors will be dropped. The strings should be the relabeld names of the coefficients.\norder is a Vector of regressor names (Strings), integers, ranges or regex that should be shown in that order. Defaults to an empty vector, in which case the order of regressors will be unchanged. Other regressors are still shown (assuming drop is empty). The strings should be the relabeld names of the coefficients.\nfixedeffects is a Vector of FE names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all FE's will be shown.\nalign is a Symbol from the set [:l,:c,:r] indicating the alignment of results columns (default :r right-aligned). Currently works only with ASCII and LaTeX output.\nheader_align is a Symbol from the set [:l,:c,:r] indicating the alignment of the header row (default :c centered). Currently works only with ASCII and LaTeX output.\nlabels is a Dict that contains displayed labels for variables (Strings) and other text in the table. If no label for a variable is found, it default to variable names. See documentation for special values.\nestimformat is a String that describes the format of the estimate.\ndigits is an Int that describes the precision to be shown in the estimate. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).\nstatisticformat is a String that describes the format of the number below the estimate (se/t).\ndigits_stats is an Int that describes the precision to be shown in the statistics. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).\nbelow_statistic is a type that describes a statistic that should be shown below each point estimate. Recognized values are nothing, StdError, TStat, and ConfInt. nothing suppresses the line. Defaults to StdError.\nregression_statistics is a Vector of types that describe statistics to be shown at the bottom of the table. Built in types are Recognized symbols are Nobs, R2, PseudoR2, R2CoxSnell, R2Nagelkerke, R2Deviance, AdjR2, AdjPseudoR2, AdjR2Deviance, DOF, LogLikelihood, AIC, AICC, BIC, FStat, FStatPValue, FStatIV, FStatIVPValue, R2Within. Defaults vary based on regression inputs (simple linear model is [Nobs, R2]).\nextralines is a Vector or a Vector{<:AbsractVector} that will be added to the end of the table. A single vector will be its own row, a vector of vectors will each be a row. Defaults to nothing.\nnumber_regressions is a Bool that governs whether regressions should be numbered. Defaults to true.\ngroups is a Vector, Vector{<:AbstractVector} or Matrix of labels used to group regressions. This can be useful if results are shown for different data sets or sample restrictions.\nprint_fe_section is a Bool that governs whether a section on fixed effects should be shown. Defaults to true.\nprint_estimator_section is a Bool that governs whether to print a section on which estimator (OLS/IV/Binomial/Poisson...) is used. Defaults to true if more than one value is displayed.\nstandardize_coef is a Bool that governs whether the table should show standardized coefficients. Note that this only works with TableRegressionModels, and that only coefficient estimates and the below_statistic are being standardized (i.e. the R^2 etc still pertain to the non-standardized regression).\nrender::AbstractRenderType is a AbstractRenderType type that governs how the table should be rendered. Standard supported types are ASCII (via AsciiTable()) and LaTeX (via LatexTable()). Defaults to AsciiTable().\nfile is a String that governs whether the table should be saved to a file. Defaults to nothing.\ntransform_labels is a Dict or one of the Symbols :ampersand, :underscore, :underscore2space, :latex\nconfint_level is a Float64 that governs the confidence level for the confidence interval. Defaults to 0.95.\n\nDetails\n\nA typical use is to pass a number of FixedEffectModels to the function, along with how it should be rendered (with render argument):\n\nregtable(regressionResult1, regressionResult2; render = AsciiTable())\n\nPass a string to the file argument to create or overwrite a file. For example, using LaTeX output,\n\nregtable(regressionResult1, regressionResult2; render = LatexTable(), file=\"myoutfile.tex\")\n\nSee the full argument list for details.\n\n\n\n\n\n","category":"function"}] +[{"location":"customization/#Customization-of-Defaults","page":"Customization and Defaults","title":"Customization of Defaults","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Pages=[\"customization.md\"]","category":"page"},{"location":"customization/#Principles","page":"Customization and Defaults","title":"Principles","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Within most publications, the tables look similar. This package tries to provide easy access to almost any setting so you can \"set and forget\" while producing tables that you need. For example, suppose you are using Latex and want to use the tabular* environment instead of the default tabular. Thanks to the Julia type system, this is possible, simply run","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.tablestart(::RegressionTables.AbstractLatex, align) = \"\\\\begin{tabular*}{\\\\linewidth}{$(align[1])@{\\\\extracolsep{\\\\fill}}$(align[2:end])}\"\nRegressionTables.tableend(::RegressionTables.AbstractLatex) = \"\\\\end{tabular*}\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"These two lines change all AbstractLatex tables.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"The Julia type system also allows customization for more individualized tables. For example, you might include some descriptive tables in a paper, but it might make sense to use different rounding for descriptive data (such as making sure floats that are stored as integers are displayed as integers or rounding for large numbers). This needs to happen without changing the rounding in most tables. To do so, you can create a new type and set the display option for that type:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"struct LatexDescriptiveTable <: RegressionTables.AbstractLatex end\nfunction Base.repr(::LatexDescriptiveTable, x::Float64; args...)\n if isinteger(x)\n format(Int(x); commas=true)\n else\n precision = x > 1000 ? 1 : 3\n format(x; commas=true, precision)\n end\nend","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Now, when creating a descriptive table (see RegressionTable for an example), pass render=LatexDescriptiveTable() to use your formatting function.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"The rest of this page goes through the defaults, showing what they are and what you might change each to.","category":"page"},{"location":"customization/#Rounding-Digits","page":"Customization and Defaults","title":"Rounding Digits","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_digits","category":"page"},{"location":"customization/#RegressionTables.default_digits","page":"Customization and Defaults","title":"RegressionTables.default_digits","text":"default_digits(render::AbstractRenderType, x::AbstractRegressionStatistic)\n\nDefault for regression statistics (R2, AIC), defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.AbstractRegressionStatistic) = 4;\n\njulia> x = R2(1.234567);\n\njulia> repr(AsciiTable(), x)\n\"1.2346\"\n\njulia> repr(LatexTable(), x)\n\"1.2346\"\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x::AbstractUnderStatistic)\n\nDefault for under statistics (TStat, StdError), defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::RegressionTables.AbstractAscii, x::RegressionTables.AbstractUnderStatistic) = 4;\n\njulia> x = StdError(1.234567);\n\njulia> repr(AsciiTable(), x)\n\"(1.2346)\"\n\njulia> repr(LatexTable(), x) # unchanged since the default_digits was only changed for Ascii\n\"(1.235)\"\n\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x::.CoefValue)\n\nDefault for CoefValue, defaults to the general setting of 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x::RegressionTables.CoefValue) = 2\n\njulia> x = RegressionTables.CoefValue(1.234567, 1); # 1 is for the p value\n\njulia> repr(HtmlTable(), x)\n\"1.23\"\n\n\n\n\n\ndefault_digits(render::AbstractRenderType, x)\n\nThe default for for all other values not otherwise specified, defaults to 3 digits\n\nExamples\n\njulia> RegressionTables.default_digits(::AbstractRenderType, x) = 4\n\njulia> x = 1.234567;\n\njulia> y = TStat(1.234567);\n\njulia> repr(LatexTable(), x)\n\"1.2346\"\n\njulia> repr(LatexTable(), y) # Also changes since the default_digits for other types default to this value\n\"(1.2346)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#Default-Keyword-Arguments","page":"Customization and Defaults","title":"Default Keyword Arguments","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_section_order\nRegressionTables.default_align\nRegressionTables.default_header_align\nRegressionTables.default_depvar\nRegressionTables.default_number_regressions\nRegressionTables.default_print_fe\nRegressionTables.default_groups\nRegressionTables.default_extralines\nRegressionTables.default_keep\nRegressionTables.default_drop\nRegressionTables.default_order\nRegressionTables.default_fixedeffects\nRegressionTables.default_labels\nRegressionTables.default_transform_labels\nRegressionTables.default_below_statistic\nRegressionTables.default_stat_below\nRegressionTables.default_render\nRegressionTables.default_file\nRegressionTables.default_print_fe_suffix\nRegressionTables.default_print_control_indicator\nRegressionTables.default_standardize_coef\nRegressionTables.default_print_estimator\nRegressionTables.default_regression_statistics\nRegressionTables.default_print_randomeffects\nRegressionTables.default_print_clusters\nRegressionTables.default_use_relabeled_values\nRegressionTables.default_confint_level","category":"page"},{"location":"customization/#RegressionTables.default_section_order","page":"Customization and Defaults","title":"RegressionTables.default_section_order","text":"default_section_order(render::AbstractRenderType)\n\nDefault section order for the table, defaults to [:groups, :depvar, :number_regressions, :break, :coef, :break, :fe, :break, :randomeffects, :break, :clusters, :break, :regtype, :break, :controls, :break, :stats, :extralines]\n\n:break is a special keyword that adds a line break between sections (e.g. between \\midrule in Latex)\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_align","page":"Customization and Defaults","title":"RegressionTables.default_align","text":"default_align(render::AbstractRenderType)\n\nDefaults to :r for all render types, possible options are :r, :l, :c This affects each part of the table after the header and the leftmost column always has the :l alignment\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_header_align","page":"Customization and Defaults","title":"RegressionTables.default_header_align","text":"default_header_align(render::AbstractRenderType)\n\nDefaults to :c for all render types, possible options are :r, :l, :c This affects the header (all sections up before :coef, see default_section_order) of each part of the table\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_depvar","page":"Customization and Defaults","title":"RegressionTables.default_depvar","text":"default_depvar(render::AbstractRenderType)\n\nDefaults to true for all render types, if false the dependent variable (\"Y\") is not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_number_regressions","page":"Customization and Defaults","title":"RegressionTables.default_number_regressions","text":"default_number_regressions(render::AbstractRenderType, rrs)\n\nDefaults to true if there is more than one regression, if false the regression number is not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_fe","page":"Customization and Defaults","title":"RegressionTables.default_print_fe","text":"default_print_fe(render::AbstractRenderType, rrs)\n\nDefaults to true, but the section will not be printed if there are not fixed effects. If false the fixed effects are not printed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_groups","page":"Customization and Defaults","title":"RegressionTables.default_groups","text":"default_groups(render::AbstractRenderType, rrs)\n\nDefaults to nothing, groups are printed above the dependent variable Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_extralines","page":"Customization and Defaults","title":"RegressionTables.default_extralines","text":"default_extralines(render::AbstractRenderType, rrs)\n\nDefaults to nothing, extra lines are printed at the end of the table. Setting a default should also use rrs (the regression results) since that determines the number of columns in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_keep","page":"Customization and Defaults","title":"RegressionTables.default_keep","text":"default_keep(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means all variables are printed. Also see Keep Drop and Order Arguments for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_drop","page":"Customization and Defaults","title":"RegressionTables.default_drop","text":"default_drop(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means no variables are dropped. Also see Keep Drop and Order Arguments for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_order","page":"Customization and Defaults","title":"RegressionTables.default_order","text":"default_order(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means the order is not changed. Also see Keep Drop and Order Arguments for more information.\n\nIn settings where the primary variables of interest are static throughout the tests, it can help to set default_order to a regex that includes that variable. For example, if the primary variables are interactions, then\n\nRegressionTables.default_order(::AbstractRenderType, rrs) = [r\" & \"]\n\nwill prioritize the interactions in the table.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_fixedeffects","page":"Customization and Defaults","title":"RegressionTables.default_fixedeffects","text":"default_fixedeffects(render::AbstractRenderType, rrs)\n\nDefaults to Vector{String}(), which means any fixed effects available are printed.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_labels","page":"Customization and Defaults","title":"RegressionTables.default_labels","text":"default_labels(render::AbstractRenderType, rrs)\n\nDefaults to Dict{String, String}(), which means no coefficients are changed. If you have a master dictionary of variables to change, it can help to set default_labels to that dictionary. It is also possible to set default_labels for each table type, allowing for labels to escape special characters in Latex.\n\nExamples\n\nRegressionTables.default_labels(render::AbstractRenderType, rrs) = Dict(\"first\" => \"New First\", \"second\" => \"X > Y\")\nRegressionTables.default_labels(render::RegressionTables.AbstractLatex, rrs) = Dict(\"first\" => \"New First\", \"second\" => \"X $>$ Y\")\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_transform_labels","page":"Customization and Defaults","title":"RegressionTables.default_transform_labels","text":"default_transform_labels(render::AbstractRenderType, rrs) = Dict{String, String}()\ndefault_transform_labels(render::AbstractLatex, rrs) = :latex\n\ntransform_labels apply a replace function to the coefficients, dependent variables and fixed effects. The default for AbstractLatex is used to escape special characters in Latex.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_below_statistic","page":"Customization and Defaults","title":"RegressionTables.default_below_statistic","text":"default_below_statistic(render::AbstractRenderType)\n\nDefaults to StdError, which means the standard error is printed below the coefficient. See AbstractUnderStatistic for more information.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_stat_below","page":"Customization and Defaults","title":"RegressionTables.default_stat_below","text":"default_stat_below(render::AbstractRenderType)\n\nDefaults to true, which means the standard error (or t-stat) is printed below the coefficient. If false, the standard error is printed to the right of the coefficient (in the same column)\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_render","page":"Customization and Defaults","title":"RegressionTables.default_render","text":"default_render(rrs)\n\nDefaults to AsciiTable(), any concrete AbstractRenderType is allowed\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_file","page":"Customization and Defaults","title":"RegressionTables.default_file","text":"default_file(render::AbstractRenderType, renderSettings::Tuple{<:AbstractRenderType, String}, rrs)\n\nDefaults to nothing, which means no file is saved.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_fe_suffix","page":"Customization and Defaults","title":"RegressionTables.default_print_fe_suffix","text":"default_print_fe_suffix(render::AbstractRenderType)\n\nWhether or not a suffix will be applied to the fixed effects, defaults to true.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_control_indicator","page":"Customization and Defaults","title":"RegressionTables.default_print_control_indicator","text":"default_print_control_indicator(render::AbstractRenderType)\n\nDefaults to true, which means if the regression has any variables ommitted (due to keep or drop), then a line is placed with Controls and Yes.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_standardize_coef","page":"Customization and Defaults","title":"RegressionTables.default_standardize_coef","text":"default_standardize_coef(render::AbstractRenderType, rrs)\n\nDefaults to false. Standardizing the coefficient divides the coefficient by its standard deviation and multiplies it by the standard deviation of the dependent variable. It is only possible for models that store the matrix, such as those in GLM.jl and MixedModels.jl. If it is not possible, the coefficients will not change.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_estimator","page":"Customization and Defaults","title":"RegressionTables.default_print_estimator","text":"default_print_estimator(render::AbstractRenderType, rrs)\n\nDefaults to true if more than one type of estimator is used. For example, if all regressions are \"OLS\", then this section will default to false, while if one regression is \"OLS\" and another is \"IV\", then this section will default to true.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_regression_statistics","page":"Customization and Defaults","title":"RegressionTables.default_regression_statistics","text":"default_regression_statistics(rr::RegressionModel)\n\nReturns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.\n\n\n\n\n\ndefault_regression_statistics(render::AbstractRenderType, rrs)\n\nDefaults to a union of the default_regression_statistics for each regression. For example, an \"OLS\" regression (with no fixed effects) will default to including [Nobs, R2], and a Probit regression will include [Nobs, PseudoR2], so the default will be [Nobs, R2, PseudoR2].\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_randomeffects","page":"Customization and Defaults","title":"RegressionTables.default_print_randomeffects","text":"default_print_randomeffects(render::AbstractRenderType, rrs)\n\nDefaults to true, but the section will not be printed if there are not random effects.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_print_clusters","page":"Customization and Defaults","title":"RegressionTables.default_print_clusters","text":"default_print_clusters(render::AbstractRenderType, rrs)\n\nDefaults to false, which means no cluster information is printed.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_use_relabeled_values","page":"Customization and Defaults","title":"RegressionTables.default_use_relabeled_values","text":"default_use_relabeled_values(render::AbstractRenderType, rrs) = true\n\nDefaults to true, which means the keep, drop and order arguments will use the relabeled values instead of the original values.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_confint_level","page":"Customization and Defaults","title":"RegressionTables.default_confint_level","text":"default_confint_level(render::AbstractRenderType, rrs)\n\nDefaults to 0.95, which means the 95% confidence interval is printed below the coefficient.\n\n\n\n\n\n","category":"function"},{"location":"customization/#Other-Defaults","page":"Customization and Defaults","title":"Other Defaults","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"While the user can adjust almost any part of this package (see How Types are Displayed), here are the remaining major defaults that are settable.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.default_breaks\nRegressionTables.default_symbol\nRegressionTables.interaction_combine\nRegressionTables.categorical_equal\nRegressionTables.random_effect_separator\nRegressionTables.estim_decorator\nRegressionTables.below_decoration\nRegressionTables.number_regressions_decoration\nRegressionTables.fe_suffix\nRegressionTables.wrapper\nRegressionTables.fe_value\nRegressionTables.cluster_suffix","category":"page"},{"location":"customization/#RegressionTables.default_breaks","page":"Customization and Defaults","title":"RegressionTables.default_breaks","text":"default_breaks(render::AbstractRenderType)\n\nThe default cutoffs for the number of \"*\" (or other value set by default_symbol). The default is [0.001, 0.01, 0.05], corresponding to a p-value of 0.1%, 1%, and 5%.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.default_symbol","page":"Customization and Defaults","title":"RegressionTables.default_symbol","text":"default_symbol(render::AbstractRenderType)\n\nThe default symbol to use for the p-value. The default is '*'.\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.interaction_combine","page":"Customization and Defaults","title":"RegressionTables.interaction_combine","text":"interaction_combine(render::AbstractRenderType) = \" & \"\ninteraction_combine(render::AbstractLatex) = \" \\$\\times\\$ \"\ninteraction_combine(render::AbstractHtml) = \" × \"\n\nUsed to separate pieces of InteractedCoefName and defaults to:\n\n\" & \" in the general case\n\"$ \\times $\" in Latex\n\" × \" in HTML\n\nChange this by rerunning the function with the desired default, for example:\n\nRegressionTables.interaction_combine(render::AbstractLatex) = \" \\& \"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.categorical_equal","page":"Customization and Defaults","title":"RegressionTables.categorical_equal","text":"categorical_equal(render::AbstractRenderType)\n\nUsed to separate the name and level of CategoricalCoefName and defaults to \": \".\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.random_effect_separator","page":"Customization and Defaults","title":"RegressionTables.random_effect_separator","text":"random_effect_separator(render::AbstractRenderType)\n\nUsed to separate the left and right hand side of RandomEffectCoefName and defaults to \" | \".\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.estim_decorator","page":"Customization and Defaults","title":"RegressionTables.estim_decorator","text":"estim_decorator(render::AbstractRenderType, s, pval; breaks=default_breaks(render), sym=default_symbol(render))\n\nDecorates a value with a symbol based on p-value. In many journals, the symbol is a * and the p-value has three cutoffs, either 0.001, 0.01, and 0.05 or 0.01, 0.05, and 0.10.\n\nIt is possible to wrap the symbols in an additional element, for example, in Latex it is common to wrap the symbol as a superscript. To do so in all tables, run\n\nRegressionTables.wrapper(::AbstractLatex, deco) = \"\\$^{$deco}\\$\"\n\nIt is also possible to change the cutoffs or symbols. To change the cutoffs, run\n\nRegressionTables.default_breaks(::AbstractLatex) = [0.01, 0.05, 0.10] # make sure these are in order\n\nAnd to change the cutoffs run\n\nRegressionTables.default_symbol(::AbstractLatex) = \"x\" # or whatever you want\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.below_decoration","page":"Customization and Defaults","title":"RegressionTables.below_decoration","text":"below_decoration(render::AbstractRenderType, s)\n\nUsed to decorate a string below the main string and defaults to \"($s)\". Change this by running:\n\nRegressionTables.below_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.number_regressions_decoration","page":"Customization and Defaults","title":"RegressionTables.number_regressions_decoration","text":"number_regressions_decoration(render::AbstractRenderType, s)\n\nUsed to decorate the regression number (e.g., \"(1)\") and defaults to \"($s)\". Change this by running:\n\nRegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.fe_suffix","page":"Customization and Defaults","title":"RegressionTables.fe_suffix","text":"fe_suffix(render::AbstractRenderType)\n\nUsed to add a suffix to the fixed effects and defaults to \" Fixed Effects\". Change this by running:\n\nRegressionTables.fe_suffix(render::AbstractRenderType) = \" Fixed Effects\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.wrapper","page":"Customization and Defaults","title":"RegressionTables.wrapper","text":"wrapper(render::AbstractRenderType, s)\n\nUsed to wrap a string, particularly for estim_decorator and defaults to s (i.e., no wrapper). For example, to wrap the stars in Latex, run:\n\nRegressionTables.wrapper(::AbstractLatex, deco) = \"$^{$deco}$\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.fe_value","page":"Customization and Defaults","title":"RegressionTables.fe_value","text":"fe_value(render::AbstractRenderType, v)\n\nDetermines how to render a yes/no value for fixed effects, defaults to \"Yes\" and \"\". Can be changed by running:\n\nRegressionTables.fe_value(render::AbstractRenderType, v) = v ? \"Yes\" : \"No\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.cluster_suffix","page":"Customization and Defaults","title":"RegressionTables.cluster_suffix","text":"cluster_suffix(render::AbstractRenderType)\n\nUsed to add a suffix to the clustering and defaults to \" Clustering\". Change this by running:\n\nRegressionTables.cluster_suffix(render::AbstractRenderType) = \" Clustering\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#Labels","page":"Customization and Defaults","title":"Labels","text":"","category":"section"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Labels are customizable by running the function that defines them. This makes it possible to change the labels once and then not worry about them on subsequent tables. To change a label, run:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label(render::AbstractRenderType, ::Type{Nobs}) = \"Obs.\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Labels use the Julia type system, so it is possible to create different labels depending on the table type. This is done by default for cases such as Nobs and R2, where the defaults for Latex and HTML are different. In such cases, it is necessary to set the label for all types that are used:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label(render::AbstractLatex, ::Type{Nobs}) = \"Obs.\"\nRegressionTables.label(render::AbstractHtml, ::Type{Nobs}) = \"Obs.\"","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Some labels (notably the R2 group), call another label function. For example:","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"label(render::AbstractRenderType, ::Type{AdjR2}) = \"Adjusted \" * label(render, R2)","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"Ths means that the label for AdjR2 relies on the label for R2. This also means that changing the label for R2 will change the labels for all other R2 types.","category":"page"},{"location":"customization/","page":"Customization and Defaults","title":"Customization and Defaults","text":"RegressionTables.label\nRegressionTables.label_p\nRegressionTables.label_ols\nRegressionTables.label_iv\nRegressionTables.label_distribution","category":"page"},{"location":"customization/#RegressionTables.label","page":"Customization and Defaults","title":"RegressionTables.label","text":"label(render::AbstractRenderType, x::Type{Nobs}) = \"N\"\nlabel(render::AbstractLatex, x::Type{Nobs}) = \"\\$N\\$\"\nlabel(render::AbstractHtml, x::Type{Nobs}) = \"N\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2}) = \"R2\"\nlabel(render::AbstractLatex, x::Type{R2}) = \"\\$R^2\\$\"\nlabel(render::AbstractHtml, x::Type{R2}) = \"R2\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2McFadden}) = \"Pseudo \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2CoxSnell}) = \"Cox-Snell \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Nagelkerke}) = \"Nagelkerke \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Deviance}) = \"Deviance \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2}) = \"Adjusted \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2McFadden}) = \"Pseudo \" * label(render, AdjR2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AdjR2Deviance}) = \"Deviance \" * label(render, AdjR2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{DOF}) = \"Degrees of Freedom\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{LogLikelihood}) = \"Log Likelihood\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AIC}) = \"AIC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{AICC}) = \"AICC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{BIC}) = \"BIC\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStat}) = \"F\"\nlabel(render::AbstractLatex, x::Type{FStat}) = \"\\$F\\$\"\nlabel(render::AbstractHtml, x::Type{FStat}) = \"F\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatPValue}) = label(render, FStat) * \"-test \" * label_p(render) * \" value\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatIV}) = \"First-stage \" * label(render, FStat) * \" statistic\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{FStatIVPValue}) = \"First-stage \" * label_p(render) * \" value\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{R2Within}) = \"Within \" * label(render, R2)\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{RegressionType}) = \"Estimator\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{HasControls}) = \"Controls\"\n\n\n\n\n\nlabel(render::AbstractRenderType, x::Type{RegressionNumbers}) = \"\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_p","page":"Customization and Defaults","title":"RegressionTables.label_p","text":"label_p(render::AbstractRenderType) = \"p\"\nlabel_p(render::AbstractLatex) = \"\\$p\\$\"\nlabel_p(render::AbstractHtml) = \"p\"\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_ols","page":"Customization and Defaults","title":"RegressionTables.label_ols","text":"label_ols(render::AbstractRenderType) = \"OLS\"\n\nAlso see RegressionType\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_iv","page":"Customization and Defaults","title":"RegressionTables.label_iv","text":"label_iv(render::AbstractRenderType) = \"IV\"\n\nAlso see RegressionType\n\n\n\n\n\n","category":"function"},{"location":"customization/#RegressionTables.label_distribution","page":"Customization and Defaults","title":"RegressionTables.label_distribution","text":"label_distribution(render::AbstractRenderType, d::D) where {D <: UnivariateDistribution} = string(Base.typename(D).wrapper)\nlabel_distribution(render::AbstractRenderType, d::NegativeBinomial) = \"Negative Binomial\"\nlabel_distribution(render::AbstractRenderType, d::InverseGaussian) = \"Inverse Gaussian\"\n\nHow to label non-linear regressions and defaults to the name of the distribution. For example, Probit will be \"Probit\". Two exceptions are given for NegativeBinomial and InverseGaussian, which are \"Negative Binomial\" and \"Inverse Gaussian\", respectively. This can be changed for a specific distribution by running:\n\nRegressionTables.label_distribution(render::AbstractRenderType, d::Poisson) = \"Poisson\"\n\n\n\n\n\n","category":"function"},{"location":"api/#API-of-Underlying-Functions","page":"API","title":"API of Underlying Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Pages=[\"api.md\"]\nDepth = 3","category":"page"},{"location":"api/#DataRow","page":"API","title":"DataRow","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.DataRow","category":"page"},{"location":"api/#RegressionTables.DataRow","page":"API","title":"RegressionTables.DataRow","text":"mutable struct DataRow{T<:AbstractRenderType} <: AbstractVector{String}\n data::Vector\n align::String\n colwidths::Vector{Int}\n print_underlines::Vector{Bool}\n render::T\nend\n\nDataRow(x::DataRow) = x\n\n(::Type{T})(x::DataRow) where {T<:AbstractRenderType} = DataRow(x.data, x.align, x.colwidths, x.print_underlines, T())\n\nfunction DataRow(\n data::Vector,\n align,\n colwidths,\n print_underlines,\n render::AbstractRenderType;\n combine_equals=false\n)\n\nfunction DataRow(\n data::Vector;\n align=\"l\" * \"r\" ^ (length(data) - 1),\n colwidths=fill(0, length(data)),\n print_underlines=zeros(Bool, length(data)),\n render::AbstractRenderType=AsciiTable(),\n combine_equals=false\n)\n\nDataRow forms the fundamental element of a RegressionTable. For a user, these can be passed as additional elements to group or extralines in regtable. A DataRow is typed with an AbstractRenderType, which controls how the DataRow is displayed. The default is AsciiTable. The DataRow contains four other elements:\n\ndata::Vector: The data to be displayed in the row. Can be individual elements (e.g., [1, 2, 3]) or pairs with a range (e.g., [1 => 1:2, 2 => 3:4]), or a combination of the two.\nalign::String: A string of ('l', 'r', 'c'), one for each element of data, indicating that elements alignment.\ncolwidths::Vector{Int}: A vector of integers, one for each element of data, indicating the width of the column. Can calculate the widths automatically using calc_widths and update them with update_widths!.\nprint_underlines::Vector{Bool}: A vector of booleans, one for each element of data, indicating whether to print an underline under the element. This is useful for printing the header of a table.\n\nDataRow is a subtype of AbstractVector{String}. It implements getindex and setindex!, which allows changing of individual elements.\n\nnote: Note\nIn most cases, it is not necessary to specify the render type for DataRow. While constructing a RegressionTable, the render type is changed to the render type of the RegressionTable.\n\nExamples\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5])\n Group 1 Group 2\n\njulia> DataRow([\"\", \"Group 1\", \"Group 1\", \"Group 2\", \"Group 2\"]; combine_equals=true)\n Group 1 Group 2\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5])\n Group 1 Group 2\n\njulia> DataRow([\" \", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; print_underlines=[false, false, true])\n Group 1 Group 2\n -------\n\njulia> DataRow([\"Group 0\", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; colwidths=[20, 20, 20], align=\"lcr\", print_underlines=true)\nGroup 0 Group 1 Group 2\n-------------------- -------------------- --------------------\n\njulia> DataRow([\"\", \"Group 1\" => 2:3, \"Group 2\" => 4:5]; render=LatexTable())\n & \\multicolumn{2}{r}{Group 1} & \\multicolumn{2}{r}{Group 2} \\\\ \n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTable","page":"API","title":"RegressionTable","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.RegressionTable","category":"page"},{"location":"api/#RegressionTables.RegressionTable","page":"API","title":"RegressionTables.RegressionTable","text":"mutable struct RegressionTable{T<:AbstractRenderType} <: AbstractMatrix{String}\n data::Vector{DataRow{T}}\n align::String\n render::T\n breaks::Vector{Int}\n colwidths::Vector{Int}\n vertical_gaps::Vector{Int}\nend\n\nRegressionTable(header::Vector, body::Matrix, args...; vargs...)\nRegressionTable(\n header::Matrix,\n body::Matrix,\n render::T=AsciiTable();\n breaks=[size(header, 1)],\n align='l' * 'r' ^ (size(header, 2) - 1),\n colwidths=fill(0, size(header, 2)),\n header_align='l' * 'c' ^ (size(header, 2) - 1),\n extralines::Vector = DataRow[]\n) where T<:AbstractRenderType\n\nThe general container. This provides some general information about the table. The DataRow handles the main printing, but this provides other necessary information, especially the break field, which is used to determine where to put the lines (e.g., \\midrule in LaTeX).\n\ndata: A vector of DataRow objects.\nalign: A string of characters, one for each column, indicating the alignment of the column. The characters are l for left, c for center, and r for right.\nrender: The render type of the table. This must be the same as the render type of the DataRow objects and is used for convenience.\nbreaks: A vector of integers, indicating where to put the lines (e.g., \\midrule in LaTeX). When displayed, the break will be placed after the line number is printed (breaks = [5] will print a break after the 5th line is printed).\ncolwidths: A vector of integers, one for each column, indicating the width of the column. Can calculate the widths automatically using calc_widths and update them with update_widths!.\nvertical_gaps: A vector of integers, indicating where to put vertical gaps in the table. These are places where two underlined cells are not connected. This is necessary for Excel integration.\n\nThe RegressionTable is a subtype of the AbstractMatrix{String} type (though this functionality is somewhat experimental). Importantly, this implements getindex and setindex!, which means individual elements of the table can be modified after its construction. See examples below. It also allows a RegressionTable to be passed to a function that expects a matrix, for example, exporting to a DataFrame DataFrames.jl would be DataFrame(Matrix(tab), :auto). Note that this will not keep multicolumn, unerlines, etc. information, but could be useful for exporting to other formats.\n\nThis type also has two convenience constructors which might be helpful if using this package to print summary statistics.\n\nExample\n\njulia> df = RDatasets.dataset(\"datasets\", \"iris\");\n\njulia> df = describe(df, :mean, :std, :q25, :median, :q75; cols=[\"SepalLength\", \"SepalWidth\", \"PetalLength\", \"PetalWidth\"]);\n\njulia> t = RegressionTables.RegressionTable(names(df), Matrix(df))\n \n----------------------------------------------------\nvariable mean std q25 median q75\n----------------------------------------------------\nSepalLength 5.843 0.828 5.100 5.800 6.400\nSepalWidth 3.057 0.436 2.800 3.000 3.300\nPetalLength 3.758 1.765 1.600 4.350 5.100\nPetalWidth 1.199 0.762 0.300 1.300 1.800\n----------------------------------------------------\n\njulia> t[1, 2] = \"Mean of Variable\";\n\njulia> t[2, 3] = 0;\n\njulia> RegressionTables.update_widths!(t); # necessary if a column gets longer\n\njulia> t\n \n---------------------------------------------------------------\nvariable Mean of Variable std q25 median q75\n---------------------------------------------------------------\nSepalLength 5.843 0 5.100 5.800 6.400\nSepalWidth 3.057 0.436 2.800 3.000 3.300\nPetalLength 3.758 1.765 1.600 4.350 5.100\nPetalWidth 1.199 0.762 0.300 1.300 1.800\n---------------------------------------------------------------\n\n\n\n\n\n","category":"type"},{"location":"api/#Render-Types","page":"API","title":"Render Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractRenderType","category":"page"},{"location":"api/#RegressionTables.AbstractRenderType","page":"API","title":"RegressionTables.AbstractRenderType","text":"abstract type AbstractRenderType end\n\nThe generic top level type for render types. Most defaults are set here unless there is a reason it needs to be set at a lower level. Subtypes are still abstract types and include AbstractAscii, AbstractLatex, and AbstractHtml.\n\n\n\n\n\n","category":"type"},{"location":"api/#Ascii","page":"API","title":"Ascii","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractAscii\nRegressionTables.AsciiTable","category":"page"},{"location":"api/#RegressionTables.AbstractAscii","page":"API","title":"RegressionTables.AbstractAscii","text":"abstract type AbstractAscii <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractAscii, so new tables (with different defaults) can be created by subtyping AbstractAscii with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.AsciiTable","page":"API","title":"RegressionTables.AsciiTable","text":"struct AsciiTable <: AbstractAscii end\n\nThe main concrete type for AbstractAscii. This is the default type used for plain text rendering.\n\n\n\n\n\n","category":"type"},{"location":"api/#Latex","page":"API","title":"Latex","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractLatex\nRegressionTables.LatexTable\nRegressionTables.LatexTableStar","category":"page"},{"location":"api/#RegressionTables.AbstractLatex","page":"API","title":"RegressionTables.AbstractLatex","text":"abstract type AbstractLatex <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractLatex, so new tables (with different defaults) can be created by subtyping AbstractLatex with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.LatexTable","page":"API","title":"RegressionTables.LatexTable","text":"struct LatexTable <: AbstractLatex end\n\nThe main concrete type for AbstractLatex. This type is used to create Latex tables.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.LatexTableStar","page":"API","title":"RegressionTables.LatexTableStar","text":"struct LatexTableStar <: AbstractLatex end\n\nAn alternative concrete type for AbstractLatex. This type is used to create Latex tables that span the entire text width.\n\n\n\n\n\n","category":"type"},{"location":"api/#HTML","page":"API","title":"HTML","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractHtml\nRegressionTables.HtmlTable","category":"page"},{"location":"api/#RegressionTables.AbstractHtml","page":"API","title":"RegressionTables.AbstractHtml","text":"abstract type AbstractHtml <: AbstractRenderType end\n\nThe abstract type for most plain text rendering. Printing is defined using the AbstractHtml, so new tables (with different defaults) can be created by subtyping AbstractHtml with minimal effort.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.HtmlTable","page":"API","title":"RegressionTables.HtmlTable","text":"struct HtmlTable <: AbstractHtml end\n\nThe main concrete type for AbstractHtml. This type is used to create HTML tables.\n\n\n\n\n\n","category":"type"},{"location":"api/#Coefficient-Naming","page":"API","title":"Coefficient Naming","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.AbstractCoefName\nRegressionTables.get_coefname","category":"page"},{"location":"api/#RegressionTables.AbstractCoefName","page":"API","title":"RegressionTables.AbstractCoefName","text":"abstract type AbstractCoefName end\n\nThese names largely mirror their equivalents in StatsModels.jl. The main difference here is that the names are always based on strings (instead of symbols). There are also several default functions that are resused (e.g., get and replace) to make relabeling coefficients easier.\n\nAbstractCoefName simply acts as a parent type to the other types. The other types are:\n\nCoefName: for Term, ContinuousTerm and FunctionTerm\nInteractedCoefName: for InteractionTerm\nCategoricalCoefName: for CategoricalTerm\nInterceptCoefName: for ConstantTerm\n\nUsing the function get_coefname will return the appropriate type for the term.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.get_coefname","page":"API","title":"RegressionTables.get_coefname","text":"get_coefname(x::AbstractTerm)::CoefName\nget_coefname(x::Term)::CoefName\nget_coefname(x::InteractionTerm)::InteractedCoefName\nget_coefname(x::InterceptTerm{H})::InterceptCoefName\nget_coefname(x::CategoricalTerm)::CategoricalCoefName\n\n\n\n\n\n","category":"function"},{"location":"api/","page":"API","title":"API","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractCoefName && t != RegressionTables.AbstractCoefName","category":"page"},{"location":"api/#RegressionTables.CategoricalCoefName","page":"API","title":"RegressionTables.CategoricalCoefName","text":"struct CategoricalCoefName <: AbstractCoefName\n name::String\n level::String\nend\n\nUsed to store the name of a coefficient for a CategoricalTerm. The level is the level of the categorical. In other words, the name is the column name, the level is the category within that column.\n\nIn a regression, the display of categorical terms is typically displayed as \"name: level\". You can change how the categorical term is \"equal\" by changing the categorical_equal function. The default is \": \", but you can change: this by setting:\n\nRegressionTables.categorical_equal(render::AbstractRenderType) = \" = \"\nRegressionTables.categorical_equal(render::AbstractLatex) = \" $=$ \"\n\nYou can also change how the categorical term is displayed by changing the repr function. The default is:\n\nBase.repr(render::AbstractRenderType, x::RegressionTables.CategoricalCoefName; args...) =\n \"$(RegressionTables.value(x))$(RegressionTables.categorical_equal(render)) $(x.level)\"\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.CoefName","page":"API","title":"RegressionTables.CoefName","text":"struct CoefName <: AbstractCoefName\n name::String\nend\n\nUsed to store the name of a coefficient. This is used for Term, ContinuousTerm and FunctionTerm.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.FixedEffectCoefName","page":"API","title":"RegressionTables.FixedEffectCoefName","text":"struct FixedEffectCoefName <: AbstractCoefName\n name::AbstractCoefName\nend\n\nUsed to store the name of a coefficient for a FixedEffectTerm. The name is the name of the fixed effect. This allows a suffix to be applied later.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.InteractedCoefName","page":"API","title":"RegressionTables.InteractedCoefName","text":"struct InteractedCoefName <: AbstractCoefName\n names::Vector\nend\n\nUsed to store the different coefficient names that makes up an InteractionTerm. The internals of the vector are typically CoefName, but can also be strings.\n\nIn a regression, each element of the vector is typically displayed as \"name1 & name2 & ...\" (in AsciiTables). The separator is set by interaction_combine function, and the default varies based on AbstractRenderType:\n\nFor AbstractAscii, it defaults to \" & \"\nFor AbstractLaTeX, it defaults to \" $\\times$ \"\nFor AbstractHtml, it defaults to \" × \"\n\nYou can change the separator by running:\n\nRegressionTables.interaction_combine(render::$RenderType) = \" & \"\n\nwhere $RenderType is the type of the renderer you want to change. For example, to change the output in AbstractLaTeX:\n\nRegressionTables.interaction_combine(::AbstractLaTeX) = \" \\& \"\n\nYou can control how interaction terms are displayed more generally by changing:\n\nBase.repr(render::AbstractRenderType, x::RegressionTables.InteractedCoefName; args...) =\n join(RegressionTables.value.(x), RegressionTables.interaction_combine(render))\n\nSee Customization of Defaults for more details.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.InterceptCoefName","page":"API","title":"RegressionTables.InterceptCoefName","text":"struct InterceptCoefName <: AbstractCoefName end\n\nUsed as a simple indicator for the existence of an intercept term. This allows relabeling of the intercept in all cases by setting\n\nRegressionTables.label(::InterceptCoefName) = \"My Intercept\"\n\nSee Customization of Defaults for more details.\n\n\n\n\n\n","category":"type"},{"location":"api/#RegressionTables.RandomEffectCoefName","page":"API","title":"RegressionTables.RandomEffectCoefName","text":"struct RandomEffectCoefName <: AbstractCoefName\n rhs::CoefName\n lhs::AbstractCoefName\n val::Float64\nend\n\nUsed to store the name and the standard deviation of a coefficient for a RandomEffectTerm from MixedModels.jl. The standard deviation is stored since that is often the useful information on the relationship between rhs and lhs.\n\n\n\n\n\n","category":"type"},{"location":"api/#Utility-Functions","page":"API","title":"Utility Functions","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"RegressionTables.calc_widths\nRegressionTables.update_widths!\nRegressionTables.value_pos\nRegressionTables.combine_other_statistics\nRegressionTables.combine_statistics\nRegressionTables.build_nm_list\nRegressionTables.reorder_nms_list\nRegressionTables.drop_names!\nRegressionTables.add_blank\nRegressionTables.missing_vars\nRegressionTables.add_element!\nRegressionTables.find_vertical_gaps\nRegressionTables.extra_cell_space","category":"page"},{"location":"api/#RegressionTables.calc_widths","page":"API","title":"RegressionTables.calc_widths","text":"calc_widths(rows::Vector{DataRow{T}}) where {T<:AbstractRenderType}\n\nCalculate the widths of each column in the table. For rows with multicolumn cells, the width of the multicolumn is divided evenly among the columns it spans.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.update_widths!","page":"API","title":"RegressionTables.update_widths!","text":"update_widths!(row::DataRow{T}, new_lengths=length.(repr.(T(), row.data))) where {T}\n\nUpdates the widths of each column in the row. If lengths are provided, then it should equate to the total number of columns in the table, not the number of elements in the row.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.value_pos","page":"API","title":"RegressionTables.value_pos","text":"value_pos(nms, x::String)\n\nReturns the position of the string x in the vector of strings or AbstractCoefName nms.\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, \"coef1\")\n1:1\n\njulia> value_pos(nms, \"coef2\")\n2:2\n\njulia> value_pos(nms, \"coef3\")\nInt64[]\n\njulia> value_pos(nms, \"coef3 & coef4\")\n4:4\n\njulia> value_pos(nms, \"(Intercept)\")\n3:3\n\njulia> value_pos(nms, \"coef1 & coef3\")\n5:5\n\njulia> value_pos(nms, \"coef5: 10\")\n6:6\n\n\n\n\n\nvalue_pos(nms, x::Int)\n\nChecks that x is a valid index for the vector of strings or AbstractCoefName nms and returns a range of x:x\n\n\n\n\n\nvalue_pos(nms, x::UnitRange)\n\nChecks that x is a valid index for the vector of strings or AbstractCoefName nms and returns x\n\n\n\n\n\nvalue_pos(nms, x::BitVector)\n\nReturns a vector of indices where x is true, called by the regex version of value_pos.\n\n\n\n\n\nvalue_pos(nms, x::Regex)\n\nLooks within each element of the vector nms (which is either a string or an AbstractCoefName) and returns a vector of indices where the regex x is found.\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, r\"coef1\") == [1, 5]\ntrue\n\njulia> value_pos(nms, r\"coef[1-3]\") == [1, 2, 4, 5]\ntrue\n\njulia> value_pos(nms, r\"coef[1-3] & coef4\") == [4]\ntrue\n\njulia> value_pos(nms, r\" & \") == [4, 5]\ntrue\n\njulia> value_pos(nms, r\"coef5\") == [6, 7]\ntrue\n\njulia> value_pos(nms, r\"coef5: 10\") == [6]\ntrue\n\n\n\n\n\nvalue_pos(nms, x::Nothing)\n\nReturns an empty vector, called by the regex and string version of value_pos.\n\n\n\n\n\nvalue_pos(nms, x::Symbol)\n\nExpects a symbol of the form :last or :end and returns the last value of nms, both are included for consistency with the Tuple\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, :last)\n7:7\n\njulia> value_pos(nms, :end)\n7:7\n\n\n\n\n\nvalue_pos(nms, x::Tuple{Symbol, Int})\n\nExpects a tuple of the form (:last, n) or (:end, n). (:last, n) returns the last n values (1:5 with (:last, 2) is 4:5), while (:end, n) returns the last index minus n (1:5 with (:end, 2) is 3).\n\nExample\n\njulia> import RegressionTables: CoefName, InterceptCoefName, InteractedCoefName, CategoricalCoefName, value_pos\n\njulia> nms = [\"coef1\", CoefName(\"coef2\"), InterceptCoefName(), InteractedCoefName([\"coef3\", \"coef4\"]), InteractedCoefName([CoefName(\"coef1\"), CoefName(\"coef3\")]), CategoricalCoefName(\"coef5\", \"10\"), CategoricalCoefName(\"coef5\", \"20\")];\n\njulia> value_pos(nms, (:last, 2))\n6:7\n\njulia> value_pos(nms, (:end, 2))\n5:5\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.combine_other_statistics","page":"API","title":"RegressionTables.combine_other_statistics","text":"combine_other_statistics(combine_other_statistics(\n stats;\n fill_val=missing,\n print_fe_suffix=true,\n fixedeffects=Vector{String}(),\n labels=Dict{String, String}(),\n transform_labels=Dict{String, String}(),\n kwargs...\n)\n\nTakes vector of nothing or statistics and combines these into a single section. These stats should be a vector of pairs, so this function expects a vector of these vector pairs or nothing. The function will combine the pairs into a single matrix. The first matrix column is a list of unique names which are the first value of the pairs and the rest of the matrix is the last value of the pairs organized.\n\nArguments\n\nstats is a Vector of Vector{Pair} or nothing that should be combined.\nfill_val defaults to missing but is the default value if the statistic is not present in that regression. For example, with fixed effects this defaults to FixedEffectValue(false) if the fixed effect is not present in the regression.\nfixedeffects is a Vector of fixed effects to include in the table. Defaults to Vector{String}(), which means all fixed effects are included. Can also be regex, integers or ranges to select which fixed effects\nprint_fe_suffix is a Bool that governs whether the fixed effects should be printed with a suffix. Defaults to true, which means the fixed effects will be printed as $X Fixed Effects. If false, the fixed effects will just be the fixed effect ($X).\nlabels is the labels from the main regtable, used to change the names of the names.\ntransform_labels is the transform_labels from the main regtable.\nkwargs are any other kwargs passed to regtable, allowing for flexible arguments for truly custom type naming.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.combine_statistics","page":"API","title":"RegressionTables.combine_statistics","text":"combine_statistics(tables, stats)\n\nTakes a set of tables (RegressionModels) and a vector of AbstractRegressionStatistic. The stats argument can also be a pair of AbstractRegressionStatistic => String, which uses the second value as the name of the statistic in the final table.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.build_nm_list","page":"API","title":"RegressionTables.build_nm_list","text":"build_nm_list(nms, keep)\n\nTakes the list of strings or AbstractCoefName nms and returns a subset of nms that contains only the elements in keep. Will also reorder the elements that are kept based on the order of keep.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.reorder_nms_list","page":"API","title":"RegressionTables.reorder_nms_list","text":"reorder_nms_list(nms, order)\n\nReorders the vector of strings or AbstractCoefName nms according to the order provided. All elements of nms are kept.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.drop_names!","page":"API","title":"RegressionTables.drop_names!","text":"drop_names!(nms, to_drop)\n\nDrops the elements of nms that are in to_drop. Does not reorder any other elements.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.add_blank","page":"API","title":"RegressionTables.add_blank","text":"add_blank(groups::Matrix, n)\nadd_blank(groups::Vector{Vector}, n)\n\nRecursively checks whether the number of columns in groups (or the length of the vector groups) is less than n, if so, add a blank column (or element) to the left of the matrix (first element of the vector).\n\nThis is used to make sure a provided piece of data is at least n columns, fitting into the table.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.missing_vars","page":"API","title":"RegressionTables.missing_vars","text":"missing_vars(table::RegressionModel, coefs::Vector)\n\nChecks whether any of the coefficients in table are not in coefs, returns true if so, false otherwise.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.add_element!","page":"API","title":"RegressionTables.add_element!","text":"add_element!(row::DataRow, val, align_i, colwidth_i, print_underline_i, i)\n\nAdds an element to the row and is only called when combine_equals=true. This means that if the last element in the row is the same as the provided val, then the last element is extended to include the new column.\n\n\n\n\n\nadd_element!(row::DataRow, val::Pair, align_i, colwidth_i, print_underline_i, i)\n\nAdds an element to the row and is only called when combine_equals=true. Since val is a pair, is is simply added to the row as is, this allows for the creation of two multicolumn cells that have the same information but are separate.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.find_vertical_gaps","page":"API","title":"RegressionTables.find_vertical_gaps","text":"find_vertical_gaps(data::Vector{DataRow{T}}) where T\n\nFinds locations in a vector of DataRow objects where there are vertical gaps. A \"vertical gap\" is a place where two underlined cells are not connected. If there is a gap, then there needs to be a space between the current column and the next column. This makes it unnecessary to have a final vertical gap since the table ends there.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.extra_cell_space","page":"API","title":"RegressionTables.extra_cell_space","text":"extra_cell_space(render::AbstractRenderType)\n\nUsed to add extra space to the cell and defaults to 0.\n\n\n\n\n\n","category":"function"},{"location":"api/#How-Types-are-Displayed","page":"API","title":"How Types are Displayed","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"This section describes how different types are displayed. Throughout this package, repr(T(), x) where T is a concrete type of AbstractRenderType is used to convert something to a string. This allows two things.","category":"page"},{"location":"api/","page":"API","title":"API","text":"Since it is easy to create new AbstractRenderTypes, it is possible to create customized displays for almost anyway situation\nSince most things in this package are types, each can be customized","category":"page"},{"location":"api/","page":"API","title":"API","text":"Base.repr","category":"page"},{"location":"api/#Base.repr","page":"API","title":"Base.repr","text":"Base.repr(render::AbstractRenderType, x; args...)\n\nWill render x as a string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Pair; args...)\n\nBy default, will render the first element of the pair according to the render type. In cases of AbstractLatex or AbstractHtml, uses the second element to determine number of columns to span.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Int; args...)\n\nBy default, will render the integer with commas\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Float64; digits=default_digits(render, x), commas=true, str_format=nothing, args...)\n\nBy default, will render the float with commas and the default number of digits. If str_format is specified, will use that instead of digits and commas\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Nothing; args...)\n\nBy default, will render the nothing as an empty string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Missing; args...)\n\nBy default, will render the missing as an empty string\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractString; args...)\n\nBy default, will render the string as is\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Bool; args...)\n\nBy default, will render the boolean as \"Yes\" or \"\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractRegressionStatistic; digits=default_digits(render, x), args...)\n\nBy default, will render the statistic with commas and the default number of digits\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractR2; digits=default_digits(render, x), args...)\n\nBy default, will render the same as AbstractRegressionStatistic\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractUnderStatistic; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses (1.234)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::ConfInt; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses (1.234, 5.678)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::CoefValue; digits=default_digits(render, x), args...)\n\nBy default, will render with the default number of digits and surrounded by parentheses and will call estim_decorator for the decoration\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: AbstractRegressionStatistic}\n\nBy default, will call the label function related to the type\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{RegressionType}; args...)\n\nBy default, will call the label function related to the RegressionType\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Tuple; args...)\n\nBy default, will render the tuple with spaces between the elements\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::AbstractCoefName; args...)\n\nBy default, will render the name of the coefficient, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::FixedEffectCoefName; args...)\n\nBy default, will render the coefficient and add \" Fixed Effects\" as a suffix, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)\n\nBy default, will render the coefficient and add the \" Clustering\" function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InteractedCoefName; args...)\n\nBy default, will render the coefficient and add the interaction_combine function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::CategoricalCoefName; args...)\n\nBy default, will render the coefficient and add the categorical_equal function as a separator, also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InterceptCoefName; args...)\n\nBy default, will render the coefficient as (Intercept), also see Regression Statistics\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::HasControls; args...)\n\nBy default, will render the same as Bool (\"Yes\" and \"\")\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RegressionNumbers; args...)\n\nBy default, will render the number in parentheses (e.g., \"(1)\", \"(2)\"...)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Type{V}; args...) where {V <: HasControls}\n\nBy default, will call the label function related to the type\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RegressionType; args...)\n\nBy default, will check if the RegressionType is an instrumental variable regression and call the label function related to the type. If it is an instrumental variable, will then call label_iv, otherwise it will call the related distribution.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::D; args...) where {D <: UnivariateDistribution}\n\nWill print the distribution as is (e.g., Probit will be \"Probit\"), unless another function overrides this behavior (e.g., Normal, InverseGaussian, NegativeBinomial)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::InverseGaussian; args...)\n\nBy default, will be \"Inverse Gaussian\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::NegativeBinomial; args...)\n\nBy default, will be \"Negative Binomial\"\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::Normal; args...)\n\nBy default, will call label_ols\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectCoefName; args...)\n\nHow to render a RandomEffectCoefName and defaults to the right hand side, then the separator, then the left hand side.\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::FixedEffectValue; args...)\n\nHow to render a FixedEffectValue and defaults to calling fe_value\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::ClusterValue; args...)\n\nHow to render a ClusterValue and defaults to how true and false is displayed.\n\nIf wanting to show the size of the clusters, run:\n\nRegressionTables.repr(render::AbstractRenderType, x::ClusterValue; args...) = repr(render, value(x); args...)\n\n\n\n\n\nBase.repr(render::AbstractRenderType, x::RandomEffectValue; args...)\n\nHow to render a RandomEffectValue and defaults to calling another render function, dependent on the type of the value\n\n\n\n\n\n","category":"function"},{"location":"api/#New-RegressionModel-Types","page":"API","title":"New RegressionModel Types","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"This package is designed to be generally compatible with the RegressionModel abstraction. It has special conditions defined around four commonly used packages (FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl). It is possible to add new models to this list, either by creating an extension for this package or by creating the necessary items in an independent package.","category":"page"},{"location":"api/","page":"API","title":"API","text":"For any new RegressionModel, there may be a need to define the following functions for the package to work correctly. Many of these will work without any issues if following the StatsModels API, and many of the others are useful for customizing how the regression result is displayed. It is also possible to redefine how RegressionTables.AbstractRegressionStatistic are displayed.","category":"page"},{"location":"api/","page":"API","title":"API","text":"RegressionTables._formula\nRegressionTables._responsename\nRegressionTables._coefnames\nRegressionTables._coef\nRegressionTables._stderror\nRegressionTables._dof_residual\nRegressionTables._pvalue\nRegressionTables.other_stats\nRegressionTables.default_regression_statistics(::RegressionModel)\nRegressionTables.can_standardize\nRegressionTables.standardize_coef_values\nRegressionTables.RegressionType","category":"page"},{"location":"api/#RegressionTables._formula","page":"API","title":"RegressionTables._formula","text":"_formula(x::RegressionModel)\n\nGenerally a passthrough for the formula function from the StatsModels package. Note tha the formula function returns the FormulaSchema.\n\nThis function is only used internally in the RegressionTables._responsename and RegressionTables._coefnames functions. Therefore, if the RegressionModel uses those two functions without using formula, this function is not necessary.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._responsename","page":"API","title":"RegressionTables._responsename","text":"_responsename(x::RegressionModel)\n\nReturns the name of the dependent variable in the regression model. The default of this returns a AbstractCoefName object, but it can be a String or Symbol as well.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._coefnames","page":"API","title":"RegressionTables._coefnames","text":"_coefnames(x::RegressionModel)\n\nReturns a vector of the names of the coefficients in the regression model. The default of this returns a vector of AbstractCoefName objects, but it can be a vector of String or Symbol as well.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._coef","page":"API","title":"RegressionTables._coef","text":"_coef(x::RegressionModel)\n\nReturns a vector of the coefficients in the regression model. By default, is just a passthrough for the coef function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._stderror","page":"API","title":"RegressionTables._stderror","text":"_stderror(x::RegressionModel)\n\nReturns a vector of the standard errors of the coefficients in the regression model. By default, is just a passthrough for the stderror function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._dof_residual","page":"API","title":"RegressionTables._dof_residual","text":"_dof_residual(x::RegressionModel)\n\nReturns the degrees of freedom of the residuals in the regression model. By default, is just a passthrough for the dof_residual function from the StatsModels package.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables._pvalue","page":"API","title":"RegressionTables._pvalue","text":"_pvalue(x::RegressionModel)\n\nReturns a vector of the p-values of the coefficients in the regression model.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.other_stats","page":"API","title":"RegressionTables.other_stats","text":"other_stats(rr::RegressionModel, s::Symbol)\n\nReturns any other statistics to be displayed. This is used (if the appropriate extension is loaded) to display the fixed effects in a FixedEffectModel (or GLFixedEffectModel), clusters in those two, or Random Effects in a MixedModel. For other regressions, this returns nothing.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.default_regression_statistics-Tuple{RegressionModel}","page":"API","title":"RegressionTables.default_regression_statistics","text":"default_regression_statistics(rr::RegressionModel)\n\nReturns a vector of AbstractRegressionStatistic objects. This is used to display the statistics in the table. This is customizable for each RegressionModel type. The default is to return a vector of Nobs and R2.\n\n\n\n\n\n","category":"method"},{"location":"api/#RegressionTables.can_standardize","page":"API","title":"RegressionTables.can_standardize","text":"can_standardize(x::RegressionModel)\n\nReturns a boolean indicating whether the coefficients can be standardized. standardized coefficients are coefficients that are scaled by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.\n\nThis is only possible of the RegressionModel includes the model matrix or the standard deviation of the dependent variable. If the RegressionModel does not include either of these, then this function should return false.\n\nSee also RegressionTables.standardize_coef_values.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.standardize_coef_values","page":"API","title":"RegressionTables.standardize_coef_values","text":"standardize_coef_values(std_X, std_Y, val)\n\nStandardizes the coefficients by the standard deviation of the variables. This is useful for comparing the relative importance of the variables in the model.\n\nThis function is only used if the RegressionTables.can_standardize function returns true.\n\nArguments\n\nstd_X::Real: The standard deviation of the independent variable.\nstd_Y::Real: The standard deviation of the dependent variable.\nval::Real: The value to be standardized (either the coefficient or the standard error).\n\nnote: Note\nIf the standard deviation of the independent variable is 0, then the interpretation of the coefficient is how many standard deviations of the dependent variable away from 0 is the intercept. In this case, the function returns val / std_Y.Otherwise, the function returns val * std_X / std_Y.\n\n\n\n\n\n","category":"function"},{"location":"api/#RegressionTables.RegressionType","page":"API","title":"RegressionTables.RegressionType","text":"struct RegressionType{T}\n val::T\n is_iv::Bool\nend\n\nThe type of the regression. val should be a distribution from the Distributions.jl package. is_iv indicates whether the regression is an instrumental variable regression. The default label for the regression type is \"Estimator\". The labels for individual regression types (e.g., \"OLS\", \"Poisson\") can be set by running:\n\nRegressionTables.label_ols(render::AbstractRenderType) = $name\nRegressionTables.label_iv(render::AbstractRenderType) = $name\n\nOr for individual distributions by running:\n\nBase.repr(render::AbstractRenderType, x::$Distribution; args...) = $Name\n\n\n\n\n\n","category":"type"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"DocTestSetup = quote # hide\n using RegressionTables, DataFrames, FixedEffectModels, RDatasets\n\n df = RDatasets.dataset(\"datasets\", \"iris\")\n df[!,:SpeciesDummy] = df[!,:Species]\n\n rr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(SpeciesDummy)))\n rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(SpeciesDummy)))\n rr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(SpeciesDummy)))\n rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(SpeciesDummy)))\n rr5 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + fe(SpeciesDummy)))\n rr6 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + SpeciesDummy))\nend # hide","category":"page"},{"location":"keep_drop_order/#Keep-Drop-and-Order-Arguments","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Pages=[\"keep_order_drop.md\"]","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"the keep, drop and order keyword arguments act similarly and allow for quick selection of coefficients to keep and change. The sequence these options are applied in is keep -> drop -> order, so coefficients kept can then be reordered. There are four fundamental ways to select arguments:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"By string\nBy Regex\nBy index number\nBy range","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"note: Note\nkeep also adjusts the order of the coefficients, so specifying keep and then order would allow order to adjust the coefficients kept. drop does not change order of coefficients.","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"note: Note\nThe keep, drop and order arguments use the relabeled versions of coefficients. If labels (or transform_labels) are used, then the keep, drop and order arguments should use the relabeled versions of the coefficients.","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Just to setup these examples:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"using RegressionTables, DataFrames, FixedEffectModels, RDatasets\n\ndf = dataset(\"datasets\", \"iris\")\ndf[!,:SpeciesDummy] = df[!,:Species]\n\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(SpeciesDummy)))\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(SpeciesDummy)))\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(SpeciesDummy)))\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(SpeciesDummy)))\nrr5 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + fe(SpeciesDummy)))\nrr6 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + SpeciesDummy))","category":"page"},{"location":"keep_drop_order/#By-String","page":"Keep Drop and Order Arguments","title":"By String","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Selecting coefficients by string is the most straightforward. Specify arguments by exactly matching the output string (if labels are not used, these are the same as the input string). For example, starting with the original example:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4)\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"To select the first two coefficients, specify keep=[\"SepalWidth\", \"PetalLength\"]:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4; keep=[\"SepalWidth\", \"PetalLength\"])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Interacted coefficients are selected using the & to separate the interactions, even if the settings use a different interaction. For example, in Latex, the interaction defaults to \\$times\\$, but would still be selected by using &:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; render=LatexTable(), keep=[\"SepalWidth & PetalLength\"])\n\n# output\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\ \n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\ \n\\midrule\nSepalWidth $\\times$ PetalLength & & & -0.070 & \\\\ \n & & & (0.041) & \\\\ \n\\midrule\nSpeciesDummy Fixed Effects & Yes & Yes & Yes & Yes \\\\ \n\\midrule\nControls & Yes & Yes & Yes & Yes \\\\ \n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\ \n$R^2$ & 0.726 & 0.863 & 0.870 & 0.635 \\\\ \nWithin-$R^2$ & 0.281 & 0.642 & 0.659 & 0.391 \\\\ \n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Similar to interacted coefficients, categorical coefficients are selected using a : between the name and the category.","category":"page"},{"location":"keep_drop_order/#By-Regex","page":"Keep Drop and Order Arguments","title":"By Regex","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Regex allows the selection of multiple coefficients based on the provided information. For example, to select coefficients with \"Width\", specify keep=[r\"Width\"]:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1,rr2,rr3,rr4; keep=[r\"Width\"])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\nSepalWidth & PetalLength -0.070\n (0.041)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Regex works similarly to the exact string argument, so if the coefficients are labeled then the regex needs to match the relabeled value. It also expects any interaction to be based on & and categorical variables to be :, for example:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6)\n\n# output\n\n \n-------------------------------------------------\n SepalLength\n --------------------\n (1) (2)\n-------------------------------------------------\nSepalWidth 0.715*** 0.432***\n (0.156) (0.081)\nPetalLength 1.050*** 0.776***\n (0.144) (0.064)\nSepalWidth & PetalLength -0.085*\n (0.040)\n(Intercept) 2.390***\n (0.262)\nSpeciesDummy: versicolor -0.956***\n (0.215)\nSpeciesDummy: virginica -1.394***\n (0.286)\n-------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n-------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n-------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6; keep=[r\": \", r\" & \"])\n\n# output\n\n \n------------------------------------------------\n SepalLength\n -------------------\n (1) (2)\n------------------------------------------------\nSpeciesDummy: versicolor -0.956***\n (0.215)\nSpeciesDummy: virginica -1.394***\n (0.286)\nSepalWidth & PetalLength -0.085*\n (0.040)\n------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n------------------------------------------------\nControls Yes Yes\n------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n------------------------------------------------","category":"page"},{"location":"keep_drop_order/#By-Integer","page":"Keep Drop and Order Arguments","title":"By Integer","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Specifying an integer allows the selection of specific coefficients:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; drop=[3])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"In typical arrays, Julia specifies the end argument to access the last argument. This does not work without direct access to the array, so this package provides an :end symbol that is handled similarly:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1, :end, (:end, 2)])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nSepalLength 0.378***\n (0.066)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"The Tuple (:end, 2) is equivalent to end-2.","category":"page"},{"location":"keep_drop_order/#By-Range","page":"Keep Drop and Order Arguments","title":"By Range","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"Specifying a range works similarly to an integer:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1:3])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPetalLength 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\nPetalWidth -0.259 0.626***\n (0.154) (0.123)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"There is also a special symbol for selecting a range at the end, :last. By itself, :last works the same as :end, but in a Tuple (:last, x) selects the last x coefficients:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; keep=[1, (:last, 2)])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\nSepalWidth 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nSepalWidth & PetalLength -0.070\n (0.041)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n------------------------------------------------------------------------\nControls Yes Yes Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n------------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/#Mixing-keep,-drop-and-order","page":"Keep Drop and Order Arguments","title":"Mixing keep, drop and order","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"As mentioned, keep is applied first, then drop and finally order. keep also will rearrange arguments, so integer and range arguments in order are applied to the resorted arguments from keep. It is also possible to mix the different types of selectors. For example, if you want the last coefficient and all coefficients that are interactions:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr5, rr6; keep=[:end, r\" & \"])\n\n# output\n\n \n------------------------------------------------\n SepalLength\n -------------------\n (1) (2)\n------------------------------------------------\nSpeciesDummy: virginica -1.394***\n (0.286)\nSepalWidth & PetalLength -0.085*\n (0.040)\n------------------------------------------------\nSpeciesDummy Fixed Effects Yes\n------------------------------------------------\nControls Yes Yes\n------------------------------------------------\nN 150 150\nR2 0.867 0.863\nWithin-R2 0.652\n------------------------------------------------","category":"page"},{"location":"keep_drop_order/#Relabeled-Coefficients","page":"Keep Drop and Order Arguments","title":"Relabeled Coefficients","text":"","category":"section"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"By default, if labels change the name of the arguments, then keep, drop and order should use the relabeled versions of the coefficients. For example, if the coefficients are relabeled:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; labels=Dict(\"SepalWidth\" => \"SW\", \"PetalLength\" => \"PL\"), keep=[\"SW\", \"PL\"])\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength SW\n ------------------------------ -------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\nSW 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPL 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n---------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n---------------------------------------------------------------------\nControls Yes Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n---------------------------------------------------------------------","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"To use the original coefficient names, set use_relabeled_values=false:","category":"page"},{"location":"keep_drop_order/","page":"Keep Drop and Order Arguments","title":"Keep Drop and Order Arguments","text":"regtable(rr1, rr2, rr3, rr4; labels=Dict(\"SepalWidth\" => \"SW\", \"PetalLength\" => \"PL\"), keep=[\"SepalWidth\", \"PetalLength\"], use_relabeled_values=false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength SW \n ------------------------------ -------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\nSW 0.804*** 0.432*** 0.719***\n (0.106) (0.081) (0.155)\nPL 0.776*** 1.047*** -0.188*\n (0.064) (0.143) (0.083)\n---------------------------------------------------------------------\nSpeciesDummy Fixed Effects Yes Yes Yes Yes\n---------------------------------------------------------------------\nControls Yes Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.726 0.863 0.870 0.635\nWithin-R2 0.281 0.642 0.659 0.391\n---------------------------------------------------------------------","category":"page"},{"location":"function_reference/#Function-Reference","page":"Function and Type Reference","title":"Function Reference","text":"","category":"section"},{"location":"function_reference/","page":"Function and Type Reference","title":"Function and Type Reference","text":"Modules = [RegressionTables]","category":"page"},{"location":"examples/#Examples","page":"Examples","title":"Examples","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Pages=[\"examples.md\"]","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Setup for the following examples:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"DocTestSetup = quote # hide\n using RegressionTables, DataFrames, RDatasets, FixedEffectModels, GLM, MixedModels;\n df = RDatasets.dataset(\"datasets\", \"iris\");\n df[!,:isSmall] = df[!,:SepalWidth] .< 2.9;\n rr1 = reg(df, @formula(SepalLength ~ SepalWidth));\n rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)));\n rr3 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength * PetalWidth + fe(Species) + fe(isSmall)));\n rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)));\n rr5 = reg(df, @formula(SepalWidth ~ SepalLength + (PetalLength ~ PetalWidth) + fe(Species)));\n rr6 = glm(@formula(isSmall ~ PetalLength + PetalWidth + Species), df, Binomial());\n rr7 = glm(@formula(isSmall ~ SepalLength + PetalLength + PetalWidth), df, Binomial());\nend # hide","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"using RegressionTables, DataFrames, RDatasets, FixedEffectModels, GLM;\ndf = dataset(\"datasets\", \"iris\");\ndf[!,:isSmall] = df[!,:SepalWidth] .< 2.9;\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth));\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)));\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength * PetalWidth + fe(Species) + fe(isSmall)));\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)));\nrr5 = reg(df, @formula(SepalWidth ~ SepalLength + (PetalLength ~ PetalWidth) + fe(Species)));\nrr6 = glm(@formula(isSmall ~ PetalLength + PetalWidth + Species), df, Binomial());\nrr7 = glm(@formula(isSmall ~ SepalLength + PetalLength + PetalWidth), df, Binomial());","category":"page"},{"location":"examples/#Default","page":"Examples","title":"Default","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4,rr5,rr6)\n\n# output\n\n \n------------------------------------------------------------------------------------------\n SepalLength SepalWidth isSmall\n ------------------------------ ------------------ ---------\n (1) (2) (3) (4) (5) (6)\n------------------------------------------------------------------------------------------\n(Intercept) 6.526*** -1.917\n (0.479) (1.242)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188* 1.048** -0.773\n (0.064) (0.129) (0.083) (0.362) (0.554)\nPetalWidth -0.625 0.626*** -3.782**\n (0.354) (0.123) (1.256)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378*** -0.313\n (0.066) (0.239)\nSpecies: versicolor 10.441***\n (1.957)\nSpecies: virginica 13.230***\n (2.636)\n------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------------------------\nEstimator OLS OLS OLS OLS IV Binomial\n------------------------------------------------------------------------------------------\nN 150 150 150 150 150 150\nR2 0.014 0.863 0.868 0.635 0.080\nWithin-R2 0.642 0.598 0.391 -0.535\nFirst-stage F statistic 19.962\nPseudo R2 0.006 0.811 0.826 0.862 0.072 0.347\n------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Below-Statistics","page":"Examples","title":"Below Statistics","text":"","category":"section"},{"location":"examples/#StdError-(default)","page":"Examples","title":"StdError (default)","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = StdError)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#No-statistics","page":"Examples","title":"No statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = nothing)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\nSepalWidth -0.223 0.432*** 0.516***\nPetalLength 0.776*** 0.723*** -0.188*\nPetalWidth -0.625 0.626***\nPetalLength & PetalWidth 0.066\nSepalLength 0.378***\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#TStat","page":"Examples","title":"TStat","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = TStat)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (13.628)\nSepalWidth -0.223 0.432*** 0.516***\n (-1.440) (5.310) (4.982)\nPetalLength 0.776*** 0.723*** -0.188*\n (12.073) (5.615) (-2.246)\nPetalWidth -0.625 0.626***\n (-1.763) (5.072)\nPetalLength & PetalWidth 0.066\n (0.981)\nSepalLength 0.378***\n (5.761)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#ConfInt-(Confidence-Interval)","page":"Examples","title":"ConfInt (Confidence Interval)","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Confidence level defaults to the 95th percentile:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = ConfInt)\n\n# output\n\n \n------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n -------------------------------------------------- ----------------\n (1) (2) (3) (4)\n------------------------------------------------------------------------------------------------\n(Intercept) 6.526***\n (5.580, 7.473)\nSepalWidth -0.223 0.432*** 0.516***\n (-0.530, 0.083) (0.271, 0.593) (0.311, 0.721)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.649, 0.903) (0.469, 0.978) (-0.353, -0.023)\nPetalWidth -0.625 0.626***\n (-1.325, 0.076) (0.382, 0.870)\nPetalLength & PetalWidth 0.066\n (-0.067, 0.199)\nSepalLength 0.378***\n (0.248, 0.507)\n------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Set the Confidence Interval level either by setting RegressionTables.default_confint_level or by adjusting the confint_level keyword argument","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; below_statistic = ConfInt, confint_level=0.9, align=:c)\n\n# output\n\n \n-------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------------------------- ----------------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------------------------------\n(Intercept) 6.526***\n (5.734, 7.319)\nSepalWidth -0.223 0.432*** 0.516***\n (-0.480, 0.033) (0.297, 0.567) (0.345, 0.688)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.669, 0.882) (0.510, 0.937) (-0.326, -0.049)\nPetalWidth -0.625 0.626***\n (-1.211, -0.038) (0.421, 0.830)\nPetalLength & PetalWidth 0.066\n (-0.045, 0.177)\nSepalLength 0.378***\n (0.269, 0.486)\n-------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n-------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Standard-Errors-on-same-line-as-coefficient","page":"Examples","title":"Standard Errors on same line as coefficient","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; stat_below=false)\n\n# output\n\n \n----------------------------------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------------------------------ ----------------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------------------------------\n(Intercept) 6.526*** (0.479)\nSepalWidth -0.223 (0.155) 0.432*** (0.081) 0.516*** (0.104)\nPetalLength 0.776*** (0.064) 0.723*** (0.129) -0.188* (0.083)\nPetalWidth -0.625 (0.354) 0.626*** (0.123)\nPetalLength & PetalWidth 0.066 (0.067)\nSepalLength 0.378*** (0.066)\n----------------------------------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------------------------------------","category":"page"},{"location":"examples/#Keep,-drop-and-order","page":"Examples","title":"Keep, drop and order","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"See Keep Drop and Order Arguments","category":"page"},{"location":"examples/#Formatting-Estimates,-Statistics-and-decimal-points","page":"Examples","title":"Formatting Estimates, Statistics and decimal points","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Also see Customization of Defaults","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; estimformat = \"%02.5f\")\n\n# output\n\n \n----------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------\n(Intercept) 6.52622***\n (0.479)\nSepalWidth -0.22336 0.43222*** 0.51611***\n (0.155) (0.081) (0.104)\nPetalLength 0.77563*** 0.72335*** -0.18757*\n (0.064) (0.129) (0.083)\nPetalWidth -0.62469 0.62571***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.06596\n (0.067)\nSepalLength 0.37777***\n (0.066)\n----------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; digits = 4)\n\n# output\n\n \n-------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------- ----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.5262***\n (0.479)\nSepalWidth -0.2234 0.4322*** 0.5161***\n (0.155) (0.081) (0.104)\nPetalLength 0.7756*** 0.7234*** -0.1876*\n (0.064) (0.129) (0.083)\nPetalWidth -0.6247 0.6257***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.0660\n (0.067)\nSepalLength 0.3778***\n (0.066)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; statisticformat = \"%02.5f\")\n\n# output\n\n \n-------------------------------------------------------------------------\n SepalLength SepalWidth\n --------------------------------- ----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.47890)\nSepalWidth -0.223 0.432*** 0.516***\n (0.15508) (0.08139) (0.10359)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.06425) (0.12883) (0.08349)\nPetalWidth -0.625 0.626***\n (0.35439) (0.12338)\nPetalLength & PetalWidth 0.066\n (0.06726)\nSepalLength 0.378***\n (0.06557)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.01382 0.86331 0.86824 0.63516\nWithin-R2 0.64151 0.59784 0.39114\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; digits_stats = 4)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.0138 0.8633 0.8682 0.6352\nWithin-R2 0.6415 0.5978 0.3911\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#Labeling-Coefficients","page":"Examples","title":"Labeling Coefficients","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"labels is applied first, transform_labels applies to within each coefficient","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2; labels = Dict(\n \"SepalLength\" => \"My dependent variable: SepalLength\",\n \"PetalLength\" => \"Length of Petal\",\n \"PetalWidth\" => \"Width of Petal\",\n \"(Intercept)\" => \"Const.\" ,\n \"isSmall\" => \"isSmall Dummies\",\n \"SpeciesDummy\" => \"Species Dummies\"\n))\n\n# output\n\n \n-----------------------------------------------------------\n My dependent variable: SepalLength\n -----------------------------------\n (1) (2)\n-----------------------------------------------------------\nConst. 6.526***\n (0.479)\nSepalWidth -0.223 0.432***\n (0.155) (0.081)\nLength of Petal 0.776***\n (0.064)\n-----------------------------------------------------------\nSpecies Fixed Effects Yes\n-----------------------------------------------------------\nN 150 150\nR2 0.014 0.863\nWithin-R2 0.642\n-----------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Each piece of an interaction term (or categorical term) is labeled based on its components:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr3; labels=Dict(\n \"SepalWidth\" => \"Sepal Width\",\n \"PetalLength\" => \"Petal Length\",\n \"PetalWidth\" => \"Petal Width\"\n)) # it is not necessary to specify a \"PetalLength & PetalWidth\" label\n\n# output\n\n \n----------------------------------------\n SepalLength\n----------------------------------------\nSepal Width 0.516***\n (0.104)\nPetal Length 0.723***\n (0.129)\nPetal Width -0.625\n (0.354)\nPetal Length & Petal Width 0.066\n (0.067)\n----------------------------------------\nSpecies Fixed Effects Yes\nisSmall Fixed Effects Yes\n----------------------------------------\nN 150\nR2 0.868\nWithin-R2 0.598\n----------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"transform_labels uses the replace function, so the name does not have to match completely:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr2, rr3; transform_labels = Dict(\"Width\" => \" Width\", \"Length\" => \" Length\"))\n\n# output\n\n \n-----------------------------------------------------------\n Sepal Length\n ------------------------------\n (1) (2) (3)\n-----------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepal Width -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetal Length 0.776*** 0.723***\n (0.064) (0.129)\nPetal Width -0.625\n (0.354)\nPetal Length & Petal Width 0.066\n (0.067)\n-----------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n-----------------------------------------------------------\nN 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\n-----------------------------------------------------------","category":"page"},{"location":"examples/#Grouping-Regressions","page":"Examples","title":"Grouping Regressions","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Groups are placed above the dependent variable names, allowing you to specify splits or some other group information. Repeated group names are automatically combined.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"grp1\", \"grp1\", \"grp2\", \"grp2\"])\n\n# output\n\n \n-------------------------------------------------------------------------\n grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"If the length of groups is one more than the number of regressions, the first element is placed in the column above the coefficient names:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"My Group:\", \"grp1\", \"grp1\", \"grp2\", \"grp2\"])\n\n# output\n\n \n-------------------------------------------------------------------------\nMy Group: grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can also specify groups with integer ranges, just note that column 1 is the column with the coefficient names:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\"My Group:\", \"grp1\" => 2:3, \"grp2\" => 4:5])\n\n# output\n\n \n-------------------------------------------------------------------------\nMy Group: grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/#Multi-Level-Groups","page":"Examples","title":"Multi-Level Groups","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Specify multiple group levels by passing a matrix or a vector of vectors:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\n \"grp parent\" \"grp parent\" \"grp parent\" \"other group\";\n \"grp1\" \"grp1\" \"grp2\" \"grp2\"\n])\n\n# output\n\n \n-------------------------------------------------------------------------\n grp parent other group\n -------------------------------- -----------\n grp1 grp2\n ------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ------------------- ---------- -----------\n (1) (2) (3) (4)\n-------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n-------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n-------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n-------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr4,rr3; groups = [\n [\"Parent Group:\", \"grp parent\" => 2:4, \"other group\"],\n [\"grp1\", \"grp1\", \"grp2\", \"grp2\"]\n])\n\n# output\n\n \n----------------------------------------------------------------------------\nParent Group: grp parent other group\n ----------------------------------- -----------\n grp1 grp2\n ---------------------- ------------------------\n SepalLength SepalWidth SepalLength\n ---------------------- ---------- -----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** -0.188* 0.723***\n (0.064) (0.083) (0.129)\nSepalLength 0.378***\n (0.066)\nPetalWidth 0.626*** -0.625\n (0.123) (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\n----------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.635 0.868\nWithin-R2 0.642 0.391 0.598\n----------------------------------------------------------------------------","category":"page"},{"location":"examples/#Do-not-print-X-block","page":"Examples","title":"Do not print $X block","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_fe_section = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_depvar = false)\n\n# output\n\n \n---------------------------------------------------------------------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; print_estimator_section = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr7; number_regressions = false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes\nisSmall Fixed Effects Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/#Re-order-Fixed-Effects","page":"Examples","title":"Re-order Fixed Effects","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Similar arguments to Keep Drop and Order Arguments (equivalent to keep before the fe_suffix is applied)","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; fixedeffects = [r\"isSmall\", \"SpeciesDummy\"])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#Change-Labels-for-Regression-Statistics","page":"Examples","title":"Change Labels for Regression Statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Also see Customization of Defaults","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; regression_statistics=[\n Nobs => \"Number of Observations\",\n R2,\n AdjR2 => \"Adj. R2\"\n])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nNumber of Observations 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nAdj. R2 0.007 0.860 0.861 0.622\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#All-Available-Statistics","page":"Examples","title":"All Available Statistics","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr5; regression_statistics = [\n Nobs,\n R2,\n PseudoR2,\n R2CoxSnell,\n R2Nagelkerke,\n R2Deviance,\n AdjR2,\n AdjPseudoR2,\n AdjR2Deviance,\n DOF,\n LogLikelihood,\n AIC,\n AICC,\n BIC,\n FStat,\n FStatPValue,\n FStatIV,\n FStatIVPValue,\n R2Within\n])\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 1.048**\n (0.064) (0.129) (0.362)\nPetalWidth -0.625\n (0.354)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -0.313\n (0.239)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nEstimator OLS OLS OLS IV\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.080\nPseudo R2 0.006 0.811 0.826 0.072\nCox-Snell R2 0.014 0.863 0.868 0.080\nNagelkerke R2 0.015 0.944 0.950 0.116\nDeviance R2 0.014 0.863 0.868 0.080\nAdjusted R2 0.007 0.860 0.861 0.055\nPseudo Adjusted R2 0.000 0.789 0.782 0.026\nDeviance Adjusted R2 0.007 0.860 0.861 0.055\nDegrees of Freedom 147 145 141 145\nLog Likelihood -182.996 -34.787 -32.031 -81.497\nAIC 367.992 73.575 72.062 166.995\nAICC 368.019 73.657 72.338 167.076\nBIC 368.019 73.657 72.338 167.076\nF 2.074 129.736 52.402 17.468\nF-test p value 0.152 0.000 0.000 0.000\nFirst-stage F statistic 19.962\nFirst-stage p value 0.000\nWithin-R2 0.642 0.598 -0.535\n----------------------------------------------------------------------","category":"page"},{"location":"examples/#LaTeX-Output","page":"Examples","title":"LaTeX Output","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable())\n\n# output\n\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\\n\\midrule\n(Intercept) & 6.526*** & & & \\\\\n & (0.479) & & & \\\\\nSepalWidth & -0.223 & 0.432*** & 0.516*** & \\\\\n & (0.155) & (0.081) & (0.104) & \\\\\nPetalLength & & 0.776*** & 0.723*** & -0.188* \\\\\n & & (0.064) & (0.129) & (0.083) \\\\\nPetalWidth & & & -0.625 & 0.626*** \\\\\n & & & (0.354) & (0.123) \\\\\nPetalLength $\\times$ PetalWidth & & & 0.066 & \\\\\n & & & (0.067) & \\\\\nSepalLength & & & & 0.378*** \\\\\n & & & & (0.066) \\\\\n\\midrule\nSpecies Fixed Effects & & Yes & Yes & Yes \\\\\nisSmall Fixed Effects & & & Yes & \\\\\n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\\n$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\\\\nWithin-$R^2$ & & 0.642 & 0.598 & 0.391 \\\\\n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"examples/#Extralines","page":"Examples","title":"Extralines","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Extralines are added to the end of a regression table","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4;\n extralines=[\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"]\n)\n\n# output\n\n \n----------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n----------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n----------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n----------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nSpecification: Option 1 Option 2 Option 3 Option 4\n----------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can specify that a single value should fill two columns, note that these will inherit the alignment from their section (so with the default align=:r, the below example would have items below the second and fourth regression):","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; extralines=[\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n [\"Difference in coefficients\", 1.503 => 2:3, 3.515 => 4:5]\n], align=:c)\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nSpecification: Option 1 Option 2 Option 3 Option 4\nDifference in coefficients 1.503 3.515\n------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"You can use the DataRow function to allow for more control, such as underlines and alignment","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; extralines=[\n DataRow([\"Difference in coefficients\", 1.5032 => 2:3, 3.5152 => 4:5]; align = \"lcc\", print_underlines=[false, true, true]),\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n])\n\n# output\n\n \n------------------------------------------------------------------------\n SepalLength SepalWidth\n ------------------------------ ----------\n (1) (2) (3) (4)\n------------------------------------------------------------------------\n(Intercept) 6.526***\n (0.479)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** -0.188*\n (0.064) (0.129) (0.083)\nPetalWidth -0.625 0.626***\n (0.354) (0.123)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength 0.378***\n (0.066)\n------------------------------------------------------------------------\nSpecies Fixed Effects Yes Yes Yes\nisSmall Fixed Effects Yes\n------------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868 0.635\nWithin-R2 0.642 0.598 0.391\nDifference in coefficients 1.503 3.515\n ------------------- ---------------------\nSpecification: Option 1 Option 2 Option 3 Option 4\n------------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"Works similarly with HTML or Latex:","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render=LatexTable(), extralines=[\n [\"Specification:\", \"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\"],\n DataRow([\"Difference in coefficients\", 1.503 => 2:3, 3.515 => 4:5]; align = \"lcc\", print_underlines=[false, true, true])\n]) # use DataRow to customize alignment\n\n# output\n\\begin{tabular}{lrrrr}\n\\toprule\n & \\multicolumn{3}{c}{SepalLength} & \\multicolumn{1}{c}{SepalWidth} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-5}\n & (1) & (2) & (3) & (4) \\\\\n\\midrule\n(Intercept) & 6.526*** & & & \\\\\n & (0.479) & & & \\\\\nSepalWidth & -0.223 & 0.432*** & 0.516*** & \\\\\n & (0.155) & (0.081) & (0.104) & \\\\\nPetalLength & & 0.776*** & 0.723*** & -0.188* \\\\\n & & (0.064) & (0.129) & (0.083) \\\\\nPetalWidth & & & -0.625 & 0.626*** \\\\\n & & & (0.354) & (0.123) \\\\\nPetalLength $\\times$ PetalWidth & & & 0.066 & \\\\\n & & & (0.067) & \\\\\nSepalLength & & & & 0.378*** \\\\\n & & & & (0.066) \\\\\n\\midrule\nSpecies Fixed Effects & & Yes & Yes & Yes \\\\\nisSmall Fixed Effects & & & Yes & \\\\\n\\midrule\n$N$ & 150 & 150 & 150 & 150 \\\\\n$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\\\\nWithin-$R^2$ & & 0.642 & 0.598 & 0.391 \\\\\nSpecification: & Option 1 & Option 2 & Option 3 & Option 4 \\\\\nDifference in coefficients & \\multicolumn{2}{c}{1.503} & \\multicolumn{2}{c}{3.515} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n\\bottomrule\n\\end{tabular}","category":"page"},{"location":"examples/#Do-Not-Print-Fixed-Effect-Suffix","page":"Examples","title":"Do Not Print Fixed Effect Suffix","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr2, rr3, rr7; print_fe_suffix=false)\n\n# output\n\n \n---------------------------------------------------------------------\n SepalLength isSmall\n ------------------------------ ---------\n (1) (2) (3) (4)\n---------------------------------------------------------------------\n(Intercept) 6.526*** 10.189***\n (0.479) (2.607)\nSepalWidth -0.223 0.432*** 0.516***\n (0.155) (0.081) (0.104)\nPetalLength 0.776*** 0.723*** 3.580***\n (0.064) (0.129) (0.708)\nPetalWidth -0.625 -3.637**\n (0.354) (1.127)\nPetalLength & PetalWidth 0.066\n (0.067)\nSepalLength -3.519***\n (0.697)\n---------------------------------------------------------------------\nSpecies Yes Yes\nisSmall Yes\n---------------------------------------------------------------------\nEstimator OLS OLS OLS Binomial\n---------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863 0.868\nWithin-R2 0.642 0.598\nPseudo R2 0.006 0.811 0.826 0.297\n---------------------------------------------------------------------","category":"page"},{"location":"examples/#Standardize-Coefficients","page":"Examples","title":"Standardize Coefficients","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Standardizing coefficients adjusts each coefficient by its standard deviation and the standard deviation of the Y variable, making the coefficients equivalent to a 1 standard deviation in X leads to a (result) standard deviation change in Y. This is only possible for regressions that store enough information to calculate these standard deviations, currently GLM.jl and MixedModels.jl. The intercept, lacking a standard deviation, is simply the number of standard deviations of Y.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"lm1 = lm(@formula(SepalLength ~ SepalWidth), df);\nlm2 = lm(@formula(SepalLength ~ SepalWidth + PetalLength + Species), df);\nregtable(lm1, lm2, rr6, rr7; standardize_coef=true)\n\n# output\n\n \n------------------------------------------------------------------\n SepalLength isSmall\n -------------------- ---------------------\n (1) (2) (3) (4)\n------------------------------------------------------------------\n(Intercept) 7.881*** 2.887*** -4.119 21.894***\n (0.578) (0.317) (2.669) (5.601)\nSepalWidth -0.118 0.228***\n (0.082) (0.043)\nPetalLength 1.654*** -2.934 13.578***\n (0.137) (2.102) (2.686)\nSpecies: versicolor -0.546*** 10.611***\n (0.123) (1.989)\nSpecies: virginica -0.796*** 13.445***\n (0.163) (2.679)\nPetalWidth -6.193** -5.957**\n (2.057) (1.846)\nSepalLength -6.260***\n (1.240)\n------------------------------------------------------------------\nEstimator OLS OLS Binomial Binomial\n------------------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.863\nPseudo R2 0.006 0.811 0.347 0.297\n------------------------------------------------------------------","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"It is also possible to standardize some coefficients and not others","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"lm1 = lm(@formula(SepalLength ~ SepalWidth), df);\nregtable(lm1, lm1, rr7, rr7; standardize_coef=[false, true, false, true])\n\n# output\n\n \n---------------------------------------------------------\n SepalLength isSmall\n ------------------- ---------------------\n (1) (2) (3) (4)\n---------------------------------------------------------\n(Intercept) 6.526*** 7.881*** 10.189*** 21.894***\n (0.479) (0.578) (2.607) (5.601)\nSepalWidth -0.223 -0.118\n (0.155) (0.082)\nSepalLength -3.519*** -6.260***\n (0.697) (1.240)\nPetalLength 3.580*** 13.578***\n (0.708) (2.686)\nPetalWidth -3.637** -5.957**\n (1.127) (1.846)\n---------------------------------------------------------\nEstimator OLS OLS Binomial Binomial\n---------------------------------------------------------\nN 150 150 150 150\nR2 0.014 0.014\nPseudo R2 0.006 0.006 0.297 0.297\n---------------------------------------------------------","category":"page"},{"location":"examples/#Show-Clustered-Standard-Errors","page":"Examples","title":"Show Clustered Standard Errors","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Displays whether or not the standard errors are clustered and in what ways.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"df_cigar = RDatasets.dataset(\"plm\", \"Cigar\");\n\nrr_c1 = reg(df_cigar, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State));\nrr_c2 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State) + fe(Year)), Vcov.cluster(:State, :Year));\nrr_c3 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State)), Vcov.cluster(:Year));\nregtable(rr_c1, rr_c2, rr_c3; print_clusters=true, labels=Dict(\"Year\" => \"Sales Year\"))\n\n# output\n\n \n----------------------------------------------------------\n Sales\n -------------------------------\n (1) (2) (3)\n----------------------------------------------------------\nNDI -0.007** -0.005* 0.002*\n (0.003) (0.003) (0.001)\nPrice -0.823** -0.413***\n (0.230) (0.084)\n----------------------------------------------------------\nState Fixed Effects Yes Yes Yes\nSales Year Fixed Effects Yes Yes\n----------------------------------------------------------\nState Clustering Yes Yes\nSales Year Clustering Yes Yes\n----------------------------------------------------------\nN 1,380 1,380 1,380\nR2 0.832 0.846 0.774\nWithin-R2 0.154 0.227 0.273\n----------------------------------------------------------","category":"page"},{"location":"examples/#MixedModels-Support","page":"Examples","title":"MixedModels Support","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"This package does support MixedModels.jl, but instead of displaying fixed effects it will display the variation from the random effects.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"form1 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + load | item) +\n (1 + spkr + prec + load | subj))\ncontr = Dict(:spkr => EffectsCoding(),\n :prec => EffectsCoding(),\n :load => EffectsCoding(),\n :item => Grouping(),\n :subj => Grouping())\n# to make sure the results are always the same, these values help fix the model into one result\nfmreθ = [\n 0.8648075226444749, 0.43344406279292136, 0.532698219245229,\n 0.03139575786126669, 0.269825335511795, 0.5307313041693793,\n 0.23438217856147925, 0.0349964462168697, 0.948766814931185,\n 0.40866263683286375, 0.6055999220729944, 0.9928229644500718,\n 0.05342261972167761,\n]\nkbm1 = updateL!(setθ!(LinearMixedModel(form1, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nform2 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + spkr + prec + load | subj))\n\nfmreθ = [\n 0.27115451643185495, 0.02114691520013967, 0.8794734878503344,\n 0.7343424423913391, 0.6603201740011742, 0.8497808579576883,\n 0.6355311618411573, 0.7807843933484198, 0.9669197738773895,\n 0.03814101806846881,\n]\nkbm2 = updateL!(setθ!(LinearMixedModel(form2, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nform3 = @formula(rt_trunc ~ 1 + spkr + prec + load +\n (1 + load | item) +\n (1 + spkr + prec * load | subj))\n\nfmreθ = [\n 0.7221403658923715, 0.3078425012729602, 0.2917886795704724,\n 0.5000142926713435, 0.7426865162047754, 0.1731367021580622,\n 0.020327890985133656, 0.7595447732279332, 0.48724482279872006,\n 0.6205745741154292, 0.3954285463498247, 0.09594315730251379,\n 0.13946651488431383, 0.6672989094861689, 0.2341117878022333,\n 0.053650218835408436, 0.143772505670828, 0.027822254707002392,\n]\nkbm3 = updateL!(setθ!(LinearMixedModel(form3, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))\nregtable(kbm1, kbm2, kbm3; labels=Dict(\n \"subj\" => \"Subject\",\n \"item\" => \"Item\",\n \"load: yes\" => \"Load\",\n \"prec: maintain\" => \"Prec\",\n \"spkr: old\" => \"Old Speaker\"\n )\n)\n\n# output\n\n \n---------------------------------------------------------------\n rt_trunc\n ---------------------------------------\n (1) (2) (3)\n---------------------------------------------------------------\n(Intercept) 2181.911*** 2182.017*** 2180.342***\n (117.772) (35.214) (58.616)\nOld Speaker 68.034 67.884 67.225*\n (53.386) (74.384) (28.793)\nPrec -333.636*** -333.785* -334.362***\n (76.398) (158.971) (66.565)\nLoad 78.532 78.426 75.764\n (167.790) (150.341) (70.860)\n---------------------------------------------------------------\nItem | (Intercept) 447.735 40.270\nItem | Load 735.081 109.918\nSubject | (Intercept) 639.374 220.685 542.038\nSubject | Old Speaker 377.476 537.692 265.104\nSubject | Prec 556.544 1,180.880 514.950\nSubject | Load 783.505 1,115.791 751.626\nSubject | Prec & Load 855.252\n---------------------------------------------------------------\nN 1,789 1,789 1,789\nLog Likelihood -14,685.198 -14,765.033 -14,711.866\n---------------------------------------------------------------","category":"page"},{"location":"examples/#Typst-Support","page":"Examples","title":"Typst Support","text":"","category":"section"},{"location":"examples/","page":"Examples","title":"Examples","text":"Similar to Latex, this package can produce Typst Tables. This requires Typst v0.11.","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1,rr2,rr3,rr4; render = TypstTable())\n\n# output\n\n#table( \n columns: (auto, auto, auto, auto, auto),\n align: (left, right, right, right, right),\n column-gutter: 1fr,\n stroke: none,\n\n table.hline(), \n [] , table.cell(colspan: 3, align: center)[SepalLength] , table.cell(colspan: 1, align: center)[SepalWidth],\n table.hline(start: 1, end: 4, stroke: 0.5pt), table.hline(start: 4, end: 5, stroke: 0.5pt),\n [] , [(1)], [(2)], [(3)], [(4)],\n table.hline(stroke: 0.7pt),\n [(Intercept)] , [6.526$\"\"^(***)$], [], [], [],\n [] , [(0.479)], [], [], [],\n [SepalWidth] , [-0.223], [0.432$\"\"^(***)$], [0.516$\"\"^(***)$], [],\n [] , [(0.155)], [(0.081)], [(0.104)], [],\n [PetalLength] , [], [0.776$\"\"^(***)$], [0.723$\"\"^(***)$], [-0.188$\"\"^(*)$],\n [] , [], [(0.064)], [(0.129)], [(0.083)],\n [PetalWidth] , [], [], [-0.625], [0.626$\"\"^(***)$],\n [] , [], [], [(0.354)], [(0.123)],\n [PetalLength $times$ PetalWidth], [], [], [0.066], [],\n [] , [], [], [(0.067)], [],\n [SepalLength] , [], [], [], [0.378$\"\"^(***)$],\n [] , [], [], [], [(0.066)],\n table.hline(stroke: 0.7pt),\n [Species Fixed Effects] , [], [Yes], [Yes], [Yes],\n [isSmall Fixed Effects] , [], [], [Yes], [],\n table.hline(stroke: 0.7pt),\n [_N_] , [150], [150], [150], [150],\n [_R_$\"\"^2$] , [0.014], [0.863], [0.868], [0.635],\n [Within-_R_$\"\"^2$] , [], [0.642], [0.598], [0.391],\n table.hline(),\n)","category":"page"},{"location":"examples/","page":"Examples","title":"Examples","text":"regtable(rr1, rr6, rr7; render = TypstTable())\n\n# output\n\n#table( \n columns: (auto, auto, auto, auto),\n align: (left, right, right, right),\n column-gutter: 1fr,\n stroke: none,\n\n table.hline(),\n [] , table.cell(colspan: 1, align: center)[SepalLength], table.cell(colspan: 2, align: center)[isSmall],\n table.hline(start: 1, end: 2, stroke: 0.5pt), table.hline(start: 2, end: 4, stroke: 0.5pt),\n [] , [(1)], [(2)], [(3)],\n table.hline(stroke: 0.7pt),\n [(Intercept)] , [6.526$\"\"^(***)$], [-1.917], [10.189$\"\"^(***)$],\n [] , [(0.479)], [(1.242)], [(2.607)],\n [SepalWidth] , [-0.223], [], [],\n [] , [(0.155)], [], [],\n [PetalLength] , [], [-0.773], [3.580$\"\"^(***)$],\n [] , [], [(0.554)], [(0.708)],\n [PetalWidth] , [], [-3.782$\"\"^(**)$], [-3.637$\"\"^(**)$],\n [] , [], [(1.256)], [(1.127)],\n [Species: versicolor], [], [10.441$\"\"^(***)$], [],\n [] , [], [(1.957)], [],\n [Species: virginica] , [], [13.230$\"\"^(***)$], [],\n [] , [], [(2.636)], [],\n [SepalLength] , [], [], [-3.519$\"\"^(***)$],\n [] , [], [], [(0.697)],\n table.hline(stroke: 0.7pt),\n [Estimator] , [OLS], [Binomial], [Binomial],\n table.hline(stroke: 0.7pt),\n [_N_] , [150], [150], [150],\n [_R_$\"\"^2$] , [0.014], [], [],\n [Pseudo _R_$\"\"^2$] , [0.006], [0.347], [0.297],\n table.hline(),\n)","category":"page"},{"location":"regression_statistics/#Regression-Statistics","page":"Regression Statistics","title":"Regression Statistics","text":"","category":"section"},{"location":"regression_statistics/#Full-Regression-Statistics","page":"Regression Statistics","title":"Full Regression Statistics","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.AbstractRegressionStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AbstractRegressionStatistic","page":"Regression Statistics","title":"RegressionTables.AbstractRegressionStatistic","text":"AbstractRegressionStatistic encapsulates all regression statistics (e.g., number of observations, R^2, etc.). In most cases, the individual regression packages provide functions that access these, generally from the StatsAPI.jl package. If the function does not exist in the regression package, it is typically added in the extension to this package. Since some statistics are not relevant for all regressions, the value of the statistic is wrapped in a Union with Nothing to indicate that the value is not available.\n\nTo define a new regression statistic, three things are needed:\n\nA new type that is a subtype of AbstractRegressionStatistic\nA constructor that takes a RegressionModel and returns the new type (or nothing if the statistic is not available)\nA label function that is dependent on the AbstractRenderType and the type provided. This label is what is displayed in the left most column of the regression table.\n\nIt is also helpful to maintain consistency by defining the value as val within the struct.\n\nFor example:\n\nstruct YMean <: RegressionTable.AbstractRegressionStatistic\n val::Union{Float64, Nothing}\nend\nYMean(x::RegressionModel) = try\n YMean(mean(x.model.rr.y))\ncatch\n YMean(nothing)\nend\nRegressionTable.label(render::AbstractRenderType, x::Type{YMean}) = \"Mean of Y\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Defined-Types","page":"Regression Statistics","title":"Defined Types","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractRegressionStatistic && t != RegressionTables.AbstractRegressionStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AIC","page":"Regression Statistics","title":"RegressionTables.AIC","text":"AIC is the Akaike Information Criterion of the regression. Labels default to \"AIC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AICC","page":"Regression Statistics","title":"RegressionTables.AICC","text":"AICC is the Corrected Akaike Information Criterion of the regression. Labels default to \"AICC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AbstractR2","page":"Regression Statistics","title":"RegressionTables.AbstractR2","text":"abstract type AbstractR2 <: AbstractRegressionStatistic end\n\nParent type for all R^2 statistics. This is available to change the formatting of all R^2 statistics. For example, if the desired display for R^2 is in the percentage term, run:\n\nBase.repr(render::AbstractRenderType, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * \"%\"\n# add second definition since Latex needs % escaped\nBase.repr(render::AbstractRenderType::RegressionTables.AbstractLatex, x::RegressionTable.AbstractR2; vargs...) = repr(render, x.val * 100; digits=2) * \"\\%\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjPseudoR2","page":"Regression Statistics","title":"RegressionTables.AdjPseudoR2","text":"See AdjR2McFadden for details.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2","page":"Regression Statistics","title":"RegressionTables.AdjR2","text":"AdjR2 is the Adjusted R^2 of the regression. Labels default to:\n\n\"Adjusted R2\" for AbstractAscii\n\"Adjusted R^2\" for AbstractLatex\n\"Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2Deviance","page":"Regression Statistics","title":"RegressionTables.AdjR2Deviance","text":"AdjR2Deviance is the Deviance Adjusted R^2 of the regression. Labels default to:\n\n\"Deviance Adjusted R2\" for AbstractAscii\n\"Deviance Adjusted R^2\" for AbstractLatex\n\"Deviance Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.AdjR2McFadden","page":"Regression Statistics","title":"RegressionTables.AdjR2McFadden","text":"AdjR2McFadden is the McFadden Adjusted R^2 of the regression (often referred to as the Pseudo Adjusted R^2). Labels default to:\n\n\"Pseudo Adjusted R2\" for AbstractAscii\n\"Pseudo Adjusted R^2\" for AbstractLatex\n\"Pseudo Adjusted R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.BIC","page":"Regression Statistics","title":"RegressionTables.BIC","text":"BIC is the Bayesian Information Criterion of the regression. Labels default to \"BIC\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.DOF","page":"Regression Statistics","title":"RegressionTables.DOF","text":"DOF is the remaining degrees of freedom in the regression. Labels default to \"Degrees of Freedom\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStat","page":"Regression Statistics","title":"RegressionTables.FStat","text":"FStat is the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"F\" for AbstractAscii\n\"F\" for AbstractLatex\n\"F\" for AbstractHtml\n\nnote: Note\nthe FStat label is used in other labels, so changing it will change those labels as well.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatIV","page":"Regression Statistics","title":"RegressionTables.FStatIV","text":"FStatIV is the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"First-stage F statistic\" for AbstractAscii\n\"First-stage F statistic\" for AbstractLatex\n\"First-stage F statistic\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatIVPValue","page":"Regression Statistics","title":"RegressionTables.FStatIVPValue","text":"FStatIVPValue is the p-value of the first-stage F-statistic of an IV regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"First-stage p value\" for AbstractAscii\n\"First-stage p value\" for AbstractLatex\n\"First-stage p value\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FStatPValue","page":"Regression Statistics","title":"RegressionTables.FStatPValue","text":"FStatPValue is the p-value of the F-statistic of the regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"F p value\" for AbstractAscii\n\"F p value\" for AbstractLatex\n\"F p value\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.LogLikelihood","page":"Regression Statistics","title":"RegressionTables.LogLikelihood","text":"LogLikelihood is the log likelihood of the regression. Labels default to \"Log Likelihood\" for all tables.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.Nobs","page":"Regression Statistics","title":"RegressionTables.Nobs","text":"Nobs is the number of observations in the regression. Labels default to:\n\n\"N\" for AbstractAscii\n\"N\" for AbstractLatex\n\"N\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.PseudoR2","page":"Regression Statistics","title":"RegressionTables.PseudoR2","text":"See R2McFadden for details.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2","page":"Regression Statistics","title":"RegressionTables.R2","text":"R2 is the R^2 of the regression. Labels default to:\n\n\"R2\" for AbstractAscii\n\"R^2\" for AbstractLatex\n\"R2\" for AbstractHtml\n\nnote: Note\nThe label for R2 is used in other related statistics. So changing the label for R2 will change the label for other R^2 statistics as well.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2CoxSnell","page":"Regression Statistics","title":"RegressionTables.R2CoxSnell","text":"R2CoxSnell is the Cox-Snell R^2 of the regression. Labels default to:\n\n\"Cox-Snell R2\" for AbstractAscii\n\"Cox-Snell R^2\" for AbstractLatex\n\"Cox-Snell R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Deviance","page":"Regression Statistics","title":"RegressionTables.R2Deviance","text":"R2Deviance is the Deviance R^2 of the regression. Labels default to:\n\n\"Deviance R2\" for AbstractAscii\n\"Deviance R^2\" for AbstractLatex\n\"Deviance R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2McFadden","page":"Regression Statistics","title":"RegressionTables.R2McFadden","text":"R2McFadden is the McFadden R^2 of the regression (often referred to as the Pseudo-R^2). Labels default to:\n\n\"Pseudo R2\" for AbstractAscii\n\"Pseudo R^2\" for AbstractLatex\n\"Pseudo R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Nagelkerke","page":"Regression Statistics","title":"RegressionTables.R2Nagelkerke","text":"R2Nagelkerke is the Nagelkerke R^2 of the regression. Labels default to:\n\n\"Nagelkerke R2\" for AbstractAscii\n\"Nagelkerke R^2\" for AbstractLatex\n\"Nagelkerke R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.R2Within","page":"Regression Statistics","title":"RegressionTables.R2Within","text":"R2Within is the within R-squared of a fixed effects regression. Since the StatsAPI.jl package does not provide a function for this, it is up to each package extension to provide the relevant information. Labels default to:\n\n\"Within R2\" for AbstractAscii\n\"Within R^2\" for AbstractLatex\n\"Within R2\" for AbstractHtml\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Below-or-Under-Statistic","page":"Regression Statistics","title":"Below or Under Statistic","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.AbstractUnderStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.AbstractUnderStatistic","page":"Regression Statistics","title":"RegressionTables.AbstractUnderStatistic","text":"abstract type AbstractUnderStatistic end\n\nThe abstract type for statistics that are below or next to the coefficients (e.g., standard errors, t-statistics, confidence intervals, etc.). The default available values are:\n\nStdError\nTStat\nConfInt\n\nNew values can be added by subtyping AbstractUnderStatistic and defining the struct and a constructor. The constructer should accept the standard error, the coefficient, and the degrees of freedom.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"Modules = [RegressionTables]\nFilter = t -> typeof(t) === DataType && t <: RegressionTables.AbstractUnderStatistic && t != RegressionTables.AbstractUnderStatistic","category":"page"},{"location":"regression_statistics/#RegressionTables.ConfInt","page":"Regression Statistics","title":"RegressionTables.ConfInt","text":"struct ConfInt <: AbstractUnderStatistic\n val::Tuple{Float64, Float64}\nend\nConfInt(rr::RegressionModel, k::Int; level=0.95, standardize=false, vargs...)\n\nThe confidence interval of a coefficient. The default confidence level is 95% (can be changed by setting RegressionTable.default_confint_level(render::AbstractRenderType, rr) = 0.90 or similar).\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.StdError","page":"Regression Statistics","title":"RegressionTables.StdError","text":"struct StdError <: AbstractUnderStatistic\n val::Float64\nend\nStdError(rr::RegressionModel, k::Int; standardize=false, vargs...)\n\nThe standard error of a coefficient.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.TStat","page":"Regression Statistics","title":"RegressionTables.TStat","text":"struct TStat <: AbstractUnderStatistic\n val::Float64\nend\nTStat(rr::RegressionModel, k::Int; vargs...)\n\nThe t-statistic of a coefficient.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#Other-Statistics","page":"Regression Statistics","title":"Other Statistics","text":"","category":"section"},{"location":"regression_statistics/","page":"Regression Statistics","title":"Regression Statistics","text":"RegressionTables.CoefValue\nRegressionTables.HasControls\nRegressionTables.RegressionNumbers\nRegressionTables.FixedEffectValue\nRegressionTables.RandomEffectValue\nRegressionTables.ClusterValue","category":"page"},{"location":"regression_statistics/#RegressionTables.CoefValue","page":"Regression Statistics","title":"RegressionTables.CoefValue","text":"struct CoefValue\n val::Float64\n pvalue::Float64\nend\n\nThe value of a coefficient and its p-value.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.HasControls","page":"Regression Statistics","title":"RegressionTables.HasControls","text":"struct HasControls\n val::Bool\nend\n\nIndicates whether the regression has coefficients left out of the table. HasControls is used as a label, which defaults to \"Controls\". This can be changed by setting\n\nRegressionTables.label(render::AbstractRenderType, x::Type{RegressionTables.HasControls}) = $name\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.RegressionNumbers","page":"Regression Statistics","title":"RegressionTables.RegressionNumbers","text":"struct RegressionNumbers\n val::Int\nend\n\nUsed to define which column number the regression is in. Primarily, this is used to control how these values are displayed. The default displays these as ($i), which can be set by running\n\nRegressionTables.number_regressions_decoration(render::AbstractRenderType, s) = \"($s)\"\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.FixedEffectValue","page":"Regression Statistics","title":"RegressionTables.FixedEffectValue","text":"struct FixedEffectValue\n val::Bool\nend\n\nA simple store of true/false for whether a fixed effect is used in the regression, used to determine how to display the value. The default is \"Yes\" and \"\", which can be changed by setting fe_value.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.RandomEffectValue","page":"Regression Statistics","title":"RegressionTables.RandomEffectValue","text":"struct RandomEffectValue\n val::Real\nend\n\nA simple sotre of the random effect value, by default equal to the standard deviation of the random effect. Typically will then be displayed the same as other Float64 values.\n\n\n\n\n\n","category":"type"},{"location":"regression_statistics/#RegressionTables.ClusterValue","page":"Regression Statistics","title":"RegressionTables.ClusterValue","text":"struct ClusterValue\n val::Int\nend\n\nA simple store of the number of clusters used in the regression. Typically will be displayed the same as other Bool values (e.g., \"Yes\" or \"\").\n\n\n\n\n\n","category":"type"},{"location":"#RegressionTables.jl","page":"Introduction","title":"RegressionTables.jl","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"This package provides publication-quality regression tables for use with FixedEffectModels.jl, GLM.jl, GLFixedEffectModels.jl and MixedModels.jl, as well as any package that implements the RegressionModel abstraction.","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"In its objective it is similar to (and heavily inspired by) the Stata command esttab and the R package stargazer.","category":"page"},{"location":"#Table-of-Contents","page":"Introduction","title":"Table of Contents","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"","category":"page"},{"location":"#Installation","page":"Introduction","title":"Installation","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"To install the package, type in the Julia command prompt","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"] add RegressionTables","category":"page"},{"location":"#A-brief-demonstration","page":"Introduction","title":"A brief demonstration","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"using RegressionTables, DataFrames, FixedEffectModels, RDatasets, GLM\n\ndf = RDatasets.dataset(\"datasets\", \"iris\")\n\nrr1 = reg(df, @formula(SepalLength ~ SepalWidth + fe(Species)))\nrr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)))\nrr3 = reg(df, @formula(SepalLength ~ SepalWidth * PetalLength + PetalWidth + fe(Species)))\nrr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)))\nrr5 = glm(@formula(SepalWidth < 2.9 ~ PetalLength + PetalWidth + Species), df, Binomial())\n\nregtable(\n rr1,rr2,rr3,rr4,rr5;\n render = AsciiTable(),\n labels = Dict(\n \"versicolor\" => \"Versicolor\",\n \"virginica\" => \"Virginica\",\n \"PetalLength\" => \"Petal Length\",\n ),\n regression_statistics = [\n Nobs => \"Obs.\",\n R2,\n R2Within,\n PseudoR2 => \"Pseudo-R2\",\n ],\n extralines = [\n [\"Main Coefficient\", \"SepalWidth\", \"SepalWidth\", \"Petal Length\", \"Petal Length\", \"Intercept\"],\n DataRow([\"Coef Diff\", 0.372 => 2:3, 1.235 => 3:4, \"\"], align=\"lccr\")\n ],\n order = [r\"Int\", r\" & \", r\": \"]\n)","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"LaTeX output can be generated by using","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable())","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Similarly, HTML tables can be created with HtmlTable().","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Send the output to a text file by passing the destination file as a keyword argument:","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable(rr1,rr2,rr3,rr4; render = LatexTable(), file=\"myoutputfile.tex\")","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"then use \\input in LaTeX to include that file in your code. Be sure to use the booktabs package:","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"\\documentclass{article}\n\\usepackage{booktabs}\n\n\\begin{document}\n\n\\begin{table}\n\\label{tab:mytable}\n\\input{myoutputfile}\n\\end{table}\n\n\\end{document}","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"regtable() can also print TableRegressionModel's from GLM.jl (and output from other packages that produce TableRegressionModel's):","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"using GLM\n\ndobson = DataFrame(Counts = [18.,17,15,20,10,20,25,13,12],\n Outcome = repeat([\"A\", \"B\", \"C\"], outer = 3),\n Treatment = repeat([\"a\",\"b\", \"c\"], inner = 3))\nrr1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)\nlm1 = fit(LinearModel, @formula(SepalLength ~ SepalWidth), df)\ngm1 = fit(GeneralizedLinearModel, @formula(Counts ~ 1 + Outcome + Treatment), dobson,\n Poisson())\n\nregtable(rr1,lm1,gm1; renderSettings = AsciiTable())","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"Printing of StatsBase.RegressionModels (e.g., MixedModels.jl and GLFixedEffectModels.jl) generally works but are less well tested; please file as issue if you encounter problems printing them.","category":"page"},{"location":"#Main-regtable-Function","page":"Introduction","title":"Main regtable Function","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"RegressionTables.regtable","category":"page"},{"location":"#RegressionTables.regtable","page":"Introduction","title":"RegressionTables.regtable","text":"Produces a publication-quality regression table, similar to Stata's esttab and R's stargazer.\n\nArguments\n\nrr::FixedEffectModel... are the FixedEffectModels from FixedEffectModels.jl that should be printed. Only required argument.\nkeep is a Vector of regressor names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all regressors will be shown. The strings should be the relabeld names of the coefficients.\ndrop is a Vector of regressor names (Strings), integers, ranges or regex that should not be shown. Defaults to an empty vector, in which case no regressors will be dropped. The strings should be the relabeld names of the coefficients.\norder is a Vector of regressor names (Strings), integers, ranges or regex that should be shown in that order. Defaults to an empty vector, in which case the order of regressors will be unchanged. Other regressors are still shown (assuming drop is empty). The strings should be the relabeld names of the coefficients.\nfixedeffects is a Vector of FE names (Strings), integers, ranges or regex that should be shown, in that order. Defaults to an empty vector, in which case all FE's will be shown.\nalign is a Symbol from the set [:l,:c,:r] indicating the alignment of results columns (default :r right-aligned). Currently works only with ASCII and LaTeX output.\nheader_align is a Symbol from the set [:l,:c,:r] indicating the alignment of the header row (default :c centered). Currently works only with ASCII and LaTeX output.\nlabels is a Dict that contains displayed labels for variables (Strings) and other text in the table. If no label for a variable is found, it default to variable names. See documentation for special values.\nestimformat is a String that describes the format of the estimate.\ndigits is an Int that describes the precision to be shown in the estimate. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).\nstatisticformat is a String that describes the format of the number below the estimate (se/t).\ndigits_stats is an Int that describes the precision to be shown in the statistics. Defaults to nothing, which means the default (3) is used (default can be changed by setting RegressionTables.default_digits(render::AbstractRenderType, x) = 3).\nbelow_statistic is a type that describes a statistic that should be shown below each point estimate. Recognized values are nothing, StdError, TStat, and ConfInt. nothing suppresses the line. Defaults to StdError.\nregression_statistics is a Vector of types that describe statistics to be shown at the bottom of the table. Built in recognized types are Nobs, R2, PseudoR2, R2CoxSnell, R2Nagelkerke, R2Deviance, AdjR2, AdjPseudoR2, AdjR2Deviance, DOF, LogLikelihood, AIC, AICC, BIC, FStat, FStatPValue, FStatIV, FStatIVPValue, R2Within. Defaults vary based on regression inputs (simple linear model is [Nobs, R2]).\nextralines is a Vector or a Vector{<:AbsractVector} that will be added to the end of the table. A single vector will be its own row, a vector of vectors will each be a row. Defaults to nothing.\nnumber_regressions is a Bool that governs whether regressions should be numbered. Defaults to true.\ngroups is a Vector, Vector{<:AbstractVector} or Matrix of labels used to group regressions. This can be useful if results are shown for different data sets or sample restrictions.\nprint_fe_section is a Bool that governs whether a section on fixed effects should be shown. Defaults to true.\nprint_estimator_section is a Bool that governs whether to print a section on which estimator (OLS/IV/Binomial/Poisson...) is used. Defaults to true if more than one value is displayed.\nstandardize_coef is a Bool that governs whether the table should show standardized coefficients. Note that this only works with TableRegressionModels, and that only coefficient estimates and the below_statistic are being standardized (i.e. the R^2 etc still pertain to the non-standardized regression).\nrender::AbstractRenderType is a AbstractRenderType type that governs how the table should be rendered. Standard supported types are ASCII (via AsciiTable()) and LaTeX (via LatexTable()). Defaults to AsciiTable().\nfile is a String that governs whether the table should be saved to a file. Defaults to nothing.\ntransform_labels is a Dict or one of the Symbols :ampersand, :underscore, :underscore2space, :latex\nconfint_level is a Float64 that governs the confidence level for the confidence interval. Defaults to 0.95.\n\nDetails\n\nA typical use is to pass a number of FixedEffectModels to the function, along with how it should be rendered (with render argument):\n\nregtable(regressionResult1, regressionResult2; render = AsciiTable())\n\nPass a string to the file argument to create or overwrite a file. For example, using LaTeX output,\n\nregtable(regressionResult1, regressionResult2; render = LatexTable(), file=\"myoutfile.tex\")\n\nSee the full argument list for details.\n\n\n\n\n\n","category":"function"}] }