refactor estimate functions; fix #75

NEWS.md

@@ -1,3 +1,7 @@
+# oldr 0.2.1
+
+
+
 # oldr 0.1.1
 
 * Created full suite of estimator functions

R/01-opIndicators.R

@@ -287,7 +287,8 @@
 #' `wgP3` | Overall 3
 #' `wgPM` | Any disability
 #'
-#' @param svy A data.frame collected using the standard RAM-OP questionnaire
+#' @param svy A [data.frame()] collected using the standard RAM-OP
+#'   questionnaire.
 #' @param indicators A character vector of indicator set names. The vector may
 #'   include one or more of the following: *"demo"*, *"food"*, *"hunger"*,
 #'   *"disability"*, *"adl"*, *"mental"*, *"dementia"*, *"health"*, *"income"*,
@@ -297,7 +298,7 @@
 #'   to report indicators for *males*, *females*, or *both* respectively.
 #'   Default is *"mf"* for both sexes.
 #'
-#' @return A tibble of older people indicators
+#' @return A [tibble()] of older people indicators.
 #'
 #' @examples
 #' # Create indicators dataset from RAM-OP survey data collected from

R/02-classicBoot.R

@@ -2,11 +2,11 @@
 #' Apply bootstrap to RAM-OP indicators using a classical estimator.
 #'
 #' @param x Indicators dataset produced by [create_op()] with primary sampling 
-#'   unit (PSU) in column named `psu`
-#' @param w A data frame with primary sampling unit (PSU) in column named `psu`
-#'   and survey weight (i.e. PSU population) in column named `pop`
-#' @param statistic A function operating on data in `x`. Fixed to `bootClassic()`
-#'   function for means
+#'   unit (PSU) in column named *"psu"*.
+#' @param w A data frame with primary sampling unit (PSU) in column named
+#'   *"psu"* and survey weight (i.e. PSU population) in column named *"pop"*.
+#' @param statistic A function operating on data in `x`. Fixed to
+#'   [bootClassic()] function for means.
 #' @param indicators A character vector of indicator set names to estimate.
 #'   Indicator set names are *"demo"*, *"food"*, *"hunger"*, *"disability"*, 
 #'   *"adl"*, *"mental"*, *"dementia"*, *"health"*, *"income"*, *"wash"*, 

R/03-probitBoot.R

@@ -2,21 +2,20 @@
 #' Apply bootstrap to RAM-OP indicators using a PROBIT estimator.
 #'
 #' @param x Indicators dataset produced by [create_op()] with primary sampling
-#'   unit (PSU) in column named `PSU`
+#'   unit (PSU) in column named *"psu"*.
 #' @param w A data frame with primary sampling unit (PSU) in column named
-#'   `psu` and survey weight (i.e. PSU population) in column named `pop`.
+#'   *"psu"* and survey weight (i.e. PSU population) in column named *"pop"*.
 #' @param gam.stat A function operating on data in `x` to estimate GAM 
-#'   prevalence for older people. Fixed to `probit_gam`
+#'   prevalence for older people. Fixed to [probit_gam()].
 #' @param sam.stat A function operating on data in `x` to estimate
-#'   SAM prevalence for older people. Fixed to `probit_sam`
+#'   SAM prevalence for older people. Fixed to [probit_sam()].
 #' @param params Parameters (named columns in `x`) passed to the function
-#'   specified in `statistic`; fixed to `MUAC` as indicator amenable
-#'   to probit estimation
-#' @param outputColumns Names of columns in output data frame; fixed to
-#'   `MUAC`
-#' @param replicates Number of bootstrap replicate case and non-case
+#'   specified in `statistic`; fixed to *"MUAC"* as indicator amenable
+#'   to probit estimation.
+#' @param outputColumns Names of columns in output data frame.
+#' @param replicates Number of bootstrap replicate case and non-case.
 #'
-#' @return A tibble of boot estimates using bootPROBIT function
+#' @return A [tibble()] of boot estimates using PROBIT.
 #'
 #' @examples
 #' test <- estimate_probit(x = indicators.ALL, w = testPSU, replicates = 3)
@@ -31,58 +30,46 @@ estimate_probit <- function(x,
                             gam.stat = probit_gam,
                             sam.stat = probit_sam,
                             params = "MUAC",
-                            outputColumns = "MUAC",
+                            outputColumns = params,
                             replicates = 399) {
   ## Blocking weighted bootstrap (GAM) - ALL
-  bootGAM.ALL <- bbw::bootBW(x = x,
-                             w = w,
-                             statistic = gam.stat,
-                             params = params,
-                             outputColumns = params,
-                             replicates = replicates)
+  bootGAM.ALL <- bootBW(
+    x = x, w = w, statistic = gam.stat, params = params, 
+    outputColumns = params, replicates = replicates
+  )
 
   ## Blocking weighted bootstrap (GAM) - MALES
-  bootGAM.MALES <- bbw::bootBW(x = x[x$sex1 == 1, ],
-                               w = w,
-                               statistic = gam.stat,
-                               params = params,
-                               outputColumns = params,
-                               replicates = replicates)
+  bootGAM.MALES <- bootBW(
+    x = x[x$sex1 == 1, ], w = w, statistic = gam.stat, params = params,
+    outputColumns = params, replicates = replicates
+  )
 
   ## Blocking weighted bootstrap (GAM) - FEMALES
-  bootGAM.FEMALES <- bbw::bootBW(x = x[x$sex2 == 1, ],
-                                 w = w,
-                                 statistic = gam.stat,
-                                 params = params,
-                                 outputColumns = params,
-                                 replicates = replicates)
+  bootGAM.FEMALES <- bootBW(
+    x = x[x$sex2 == 1, ], w = w, statistic = gam.stat, params = params,
+    outputColumns = params, replicates = replicates
+  )
 
   ## Rename results
   names(bootGAM.ALL) <- names(bootGAM.MALES) <- names(bootGAM.FEMALES) <- "GAM"
 
   ## Blocking weighted bootstrap (SAM) - ALL
-  bootSAM.ALL <- bbw::bootBW(x = x,
-                             w = w,
-                             statistic = sam.stat,
-                             params = params,
-                             outputColumns = params,
-                             replicates = replicates)
+  bootSAM.ALL <- bootBW(
+    x = x, w = w, statistic = sam.stat, params = params,
+    outputColumns = params, replicates = replicates
+  )
 
   ## Blocking weighted bootstrap (SAM) - MALES
-  bootSAM.MALES <- bbw::bootBW(x = x[x$sex1 ==1, ],
-                               w = w,
-                               statistic = sam.stat,
-                               params = params,
-                               outputColumns = params,
-                               replicates = replicates)
+  bootSAM.MALES <- bootBW(
+    x = x[x$sex1 ==1, ], w = w, statistic = sam.stat, params = params,
+    outputColumns = params, replicates = replicates
+  )
 
   ## Blocking weighted bootstrap (SAM) - FEMALES
-  bootSAM.FEMALES <- bbw::bootBW(x = x[x$sex2 ==1, ],
-                                 w = w,
-                                 statistic = sam.stat,
-                                 params = params,
-                                 outputColumns = params,
-                                 replicates = replicates)
+  bootSAM.FEMALES <- bootBW(
+    x = x[x$sex2 ==1, ], w = w, statistic = sam.stat, params = params,
+    outputColumns = params, replicates = replicates
+  )
 
   ## Rename results
   names(bootSAM.ALL) <- names(bootSAM.MALES) <- names(bootSAM.FEMALES) <- "SAM"
@@ -111,37 +98,46 @@ estimate_probit <- function(x,
 
   ## Extract estimates from 'boot.ALL' data.frames
   estimates.ALL <- data.frame(
-    t(apply(X = boot.ALL,
-            MARGIN = 2,
-            FUN = quantile,
-            probs = c(0.025, 0.5, 0.975), na.rm = TRUE)))
+    t(
+      apply(
+        X = boot.ALL, MARGIN = 2, FUN = quantile, 
+        probs = c(0.025, 0.5, 0.975), na.rm = TRUE
+      )
+    )
+  )
 
   ## Extract estimates from 'boot.MALES' data.frames
   estimates.MALES <- data.frame(
-    t(apply(X = boot.MALES,
-            MARGIN = 2,
-            FUN = quantile,
-            probs = c(0.025, 0.5, 0.975), na.rm = TRUE)))
+    t(
+      apply(
+        X = boot.MALES, MARGIN = 2, FUN = quantile,
+        probs = c(0.025, 0.5, 0.975), na.rm = TRUE
+      )
+    )
+  )
 
   ## Extract estimates from 'boot.FEMALES' data.frames
   estimates.FEMALES <- data.frame(
-    t(apply(X = boot.FEMALES,
-            MARGIN = 2,
-            FUN = quantile,
-            probs = c(0.025, 0.5, 0.975), na.rm = TRUE)))
+    t(
+      apply(
+        X = boot.FEMALES, MARGIN = 2, FUN = quantile,
+        probs = c(0.025, 0.5, 0.975), na.rm = TRUE
+      )
+    )
+  )
 
   ## Join 'estimates.*' data.frames side-by-side
-  probitEstimates <- data.frame(estimates.ALL,
-                                estimates.MALES,
-                                estimates.FEMALES)
+  probitEstimates <- data.frame(
+    estimates.ALL, estimates.MALES, estimates.FEMALES
+  )
 
   ## Clean-up row and column names
   probitEstimates$indicator <- row.names(probitEstimates)
   row.names(probitEstimates) <- NULL
-  names(probitEstimates) <- c("LCL.ALL", "EST.ALL", "UCL.ALL",
-                              "LCL.MALES", "EST.MALES", "UCL.MALES",
-                              "LCL.FEMALES", "EST.FEMALES", "UCL.FEMALES",
-                              "INDICATOR")
+  names(probitEstimates) <- c(
+    "LCL.ALL", "EST.ALL", "UCL.ALL", "LCL.MALES", "EST.MALES", "UCL.MALES",
+    "LCL.FEMALES", "EST.FEMALES", "UCL.FEMALES", "INDICATOR"
+  )
 
   ## Re-order columns
   col_order <- c(

R/04-probit_muac.R

@@ -1,13 +1,14 @@
 #' 
 #' PROBIT statistics function for bootstrap estimation of older people GAM
 #'
-#' @param x A data frame with `primary sampling unit (PSU)` in column named
-#'   `psu` and with data column/s containing the continuous variable/s of
+#' @param x A data frame with primary sampling unit (PSU) in column named
+#'   *"psu"* and with data column/s containing the continuous variable/s of
 #'   interest with column names corresponding to `params` values
 #' @param params A vector of column names corresponding to the continuous
 #'   variables of interest contained in `x`
 #' @param threshold cut-off value for continuous variable to differentiate
-#'   case and non-case. Default is set at 210.
+#'   case and non-case. Default is set at 210 for [probit_gam()] and 185 for
+#'   [probit_sam()].
 #'
 #' @return A numeric vector of the PROBIT estimate of each continuous variable
 #'   of interest with length equal to `length(params)`

R/06-estimate_op.R

@@ -2,16 +2,17 @@
 #' Estimate all standard RAM-OP indicators
 #'
 #' @param x Indicators dataset produced by [create_op()] with primary sampling
-#'   unit (PSU) in column named `PSU`
+#'   unit (PSU) in column named *"psu"*`
 #' @param w A data frame with primary sampling unit (PSU) in column named
-#'   `psu` and survey weight (i.e. PSU population) in column named `pop`
+#'   *"psu"* and survey weight (i.e. PSU population) in column named *"pop"*.
 #' @param indicators A character vector of indicator set names to estimate.
 #'   Indicator set names are *"demo"*, *"anthro"*, *"food"*, *"hunger"*,
 #'   *"disability"*, *"adl"*, *"mental"*, *"dementia"*, *"health"*, *"income"*, 
 #'   *"wash"*, *"visual"*, and *"misc"*. Default is all indicator sets.
 #' @param replicates Number of bootstrap replicates. Default is 399.
 #'
-#' @return Tibble of boot estimates for all specified standard RAM-OP indicators
+#' @return A [tibble()] of boot estimates for all specified standard RAM-OP
+#'   indicators
 #'
 #' @examples
 #' estimate_op(x = create_op(testSVY), w = testPSU, replicates = 9)

inst/WORDLIST

@@ -11,13 +11,11 @@ Codecov
 Colpe
 DDS
 DK
-DS
 Deitchler
 Dop
 EpiData
 FANTA
 FAO
-FG
 FHI
 Grotz
 HH
@@ -31,7 +29,6 @@ Jaffe
 LJ
 Lifecycle
 MAM
-MF
 MUAC
 Moskowitz
 Mroczek
@@ -49,34 +46,21 @@ RW
 RapidSurveys
 SLT
 TD
-Tibble
 WG
 adl
 anthro
 bootClassic
-bootPROBIT
 cd
 ci
-classADL
 codecov
 etc
 frac
-hasHelp
 mf
 moringa
 poorVA
 psu
-resp
-scoreADL
+ramOP
+ramOPreport
 th
 tibble
-unmetNeed
-wgCommunicatingD
-wgHearingD
-wgMobilityD
-wgP
-wgPM
-wgRememberingD
-wgSelfCareD
-wgVisionD
 widehat