knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures", out.width = "100%" ) library(hausekeep) # load package
To install the package, type the following commands into the R console:
# install.packages("devtools") devtools::install_github("hauselin/hausekeep") # you might have to install devtools first (see above)
summaryh()
generates formatted results and effect sizes for manuscriptsGenerate model summaries that can be copied and pasted straight into your manuscript (no more copy-paste frustrations and errors!). Summaries are formatted according to American Psychological Association (APA) guidelines (get in touch if you require other formats). Example APA summaries generated by summaryh()
:
b = 1.41, SE = 0.56, t(30) = 2.53, p = .017, r = 0.42
F(1, 30) = 9.00, p = .005, r = 0.48
t(23) = −4.67, p < .001, r = 0.70
See documentation for optional parameters.
model_lm <- lm(mpg ~ cyl, mtcars) summary(model_lm) # base R summary() summaryh(model_lm) # returns APA-formatted output in a data.table # linear mixed effects regression library(lme4); library(lmerTest) # load packages to fit mixed effects models model <- lmer(weight ~ Time * Diet + (1 + Time | Chick), data = ChickWeight) summary(model) # standard summary summaryh(model) # ANOVA summaryh(aov(mpg ~ gear, mtcars)) # correlation cor.test(mtcars$mpg, mtcars$cyl) summaryh(cor.test(mtcars$mpg, mtcars$cyl))
es()
converts between effect size measuresThe es
function converts one effect size into other effect sizes (e.g., d, r, R2, f, odds ratio, log odds ratio, area-under-curve [AUC]). Also available at https://www.escal.site.
es(d = 0.2) es(r = c(0.1, 0.4, 0.7))
outliers_mad()
identifies outliers using robust median absolute deviation approachexample <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier outliers_mad(example) # MAD approach
outliersZ()
identifies outliers using Z-score cut-offexample <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier outliersZ(example) # SD approach # compare with MAD approach from above outliersZ(example) # SD approach
fit_ezddm()
fits EZ-diffusion model for two-choice response time taskslibrary(rtdists) # load package to help us simulate some data data1 <- rdiffusion(n = 100, a = 2, v = 0.3, t0 = 0.5, z = 0.5 * 2) # simulate data data2 <- rdiffusion(n = 100, a = 2, v = -0.3, t0 = 0.5, z = 0.5 * 2) # simulate data dataAll <- rbind(data1, data2) # join data dataAll$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()
fits every possible linear regression model given a set of predictors and covariatessca_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")
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