mcr_test: Perform a Monte Carlo randomization test
In holub008/nymph: Randomization tests for nonparametric inference

Description Usage Arguments Author(s) Examples

Simulate a permutation test of arbitrary statistics using a simple randomization (i.e. non-clustered) scheme Note that mcrd_test and mcr_anova convenience functions cover common use cases and should be preferred for interactive, non-programmatic use If the "parallel" pacakge is installed, use the global mc.cores option to control level of parallelism

1 2	mcr_test(data, statistics, group_var, statistic_names = names(statistics), trials = 1000)

`data`	a data frame to draw statistics from
`statistics`	a function or vector of functions accepting a data frame, returning a scalar statistic
`group_var`	a character, singular column name in the data representing groups.
`statistic_names`	the names of statistics that are computed. must be specified if the supplied statistics do not have names
`trials`	the number of Monte Carlo trials to perform

kholub

library(dplyr)
data(mpg)
test_data <- mpg %>% filter(class %in% c('suv', 'compact'))
mcr_res <- mcr_test(test_data, c(median_difference = function(df) {
                                                                      group_medians <- df %>%
                                                                        group_by(class) %>%
                                                                        summarize(
                                                                          median = median(hwy))
                                                                      as.numeric(group_medians[group_medians$class == 'suv', 'median']) -
                                                                         as.numeric(group_medians[group_medians$class == 'compact', 'median'])
                                                                     }),
                      'class')