Functions to make it easier to analyse and summarise data and results in R. For documentation, see https://hauselin.github.io/hausekeep/ Also check out my R tutorials here.
To install the package, type the following commands into the R console:
# install.packages("devtools")
devtools::install_github("hauselin/hausekeep") # you might have to install devtools first (see above)Generate model summaries that can be copied and pasted straight into
your manuscript (no more copy-paste frustrations and errors!). Summaries
are formatted according to American Psychological Association (APA)
guidelines (get in touch if you require other formats). Example APA
summaries generated by summaryh():
- regression output:
b = 1.41, SE = 0.56, t(30) = 2.53, p = .017, r = 0.42 - ANOVA output:
F(1, 30) = 9.00, p = .005, r = 0.48 - t-test output:
t(23) = −4.67, p < .001, r = 0.70
See documentation for optional parameters.
model_lm <- lm(mpg ~ cyl, mtcars)
summary(model_lm) # base R summary()
summaryh(model_lm) # returns APA-formatted output in a data.table
# linear mixed effects regression
library(lme4); library(lmerTest) # load packages to fit mixed effects models
model <- lmer(weight ~ Time * Diet + (1 + Time | Chick), data = ChickWeight)
summary(model) # standard summary
summaryh(model)
# ANOVA
summaryh(aov(mpg ~ gear, mtcars))
# correlation
cor.test(mtcars$mpg, mtcars$cyl)
summaryh(cor.test(mtcars$mpg, mtcars$cyl))The es function converts one effect size into other effect sizes
(e.g., d, r, R2, f, odds ratio, log odds ratio,
area-under-curve [AUC]). Also available at https://www.escal.site.
es(d = 0.2)
#> d: 0.2
#> d r R2 f oddsratio logoddsratio auc fishersz
#> 1 0.2 0.1 0.01 0.1 1.437 0.363 0.556 0.1
es(r = c(0.1, 0.4, 0.7))
#> r: 0.1 r: 0.4 r: 0.7
#> d r R2 f oddsratio logoddsratio auc fishersz
#> 1 0.201 0.1 0.01 0.101 1.440 0.365 0.557 0.100
#> 2 0.873 0.4 0.16 0.436 4.871 1.583 0.731 0.424
#> 3 1.960 0.7 0.49 0.980 35.014 3.556 0.917 0.867example <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier
outliers_mad(example) # MAD approach
#> [1] 1 3 3 6 8 10 10 NAexample <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier
outliersZ(example) # SD approach
#> [1] 1 3 3 6 8 10 10 NA
# compare with MAD approach from above
outliersZ(example) # SD approach
#> [1] 1 3 3 6 8 10 10 NAlibrary(rtdists) # load package to help us simulate some data
data1 <- rdiffusion(n = 100, a = 2, v = 0.3, t0 = 0.5, z = 0.5 * 2) # simulate data
data2 <- rdiffusion(n = 100, a = 2, v = -0.3, t0 = 0.5, z = 0.5 * 2) # simulate data
dataAll <- rbind(data1, data2) # join data
dataAll$response <- ifelse(dataAll$response == "upper", 1, 0) # convert responses to 1 and 0
dataAll$subject <- rep(c(1, 2), each = 100) # assign subject id
dataAll$cond1 <- sample(c("a", "b"), 200, replace = T) # randomly assign conditions a/b
dataAll$cond2 <- sample(c("y", "z"), 200, replace = T) # randomly assign conditions y/z
# fit model to just entire data set (assumes all data came from 1 subject)
fit_ezddm(data = dataAll, rts = "rt", responses = "response")
# fit model to each subject (no conditions)
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject")
# fit model to each subject by cond1
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject", group = "cond1")
# fit model to each subject by cond1,cond2
fit_ezddm(data = dataAll, rts = "rt", responses = "response", id = "subject", group = c("cond1", "cond2"))sca_lm() is a basic implementation of specification curve
analysis for linear
regression.
# models to fit: mpg ~ cyl; mpg ~ carb; mpg ~ cyl + carb
sca_lm(data = mtcars, dv = "mpg", ivs = c("cyl", "carb")) # default no covariates
# models to fit (with and without covariate vs): mpg ~ cyl; mpg ~ carb; mpg ~ cyl + carb
sca_lm(data = mtcars, dv = "mpg", ivs = c("cyl", "carb"), covariates = "vs")