permutations  R Documentation 
A permutation sample is the same size as the original data set and is made
by permuting/shuffling one or more columns. This results in analysis
samples where some columns are in their original order and some columns
are permuted to a random order. Unlike other sampling functions in
rsample
, there is no assessment set and calling assessment()
on a
permutation split will throw an error.
permutations(data, permute = NULL, times = 25, apparent = FALSE, ...)
data 
A data frame. 
permute 
One or more columns to shuffle. This argument supports

times 
The number of permutation samples. 
apparent 
A logical. Should an extra resample be added where the analysis is the standard data set. 
... 
Not currently used. 
The argument apparent
enables the option of an additional
"resample" where the analysis data set is the same as the original data
set. Permutationbased resampling can be especially helpful for computing
a statistic under the null hypothesis (e.g. tstatistic). This forms the
basis of a permutation test, which computes a test statistic under all
possible permutations of the data.
A tibble
with classes permutations
, rset
, tbl_df
, tbl
, and
data.frame
. The results include a column for the data split objects and a
column called id
that has a character string with the resample
identifier.
permutations(mtcars, mpg, times = 2) permutations(mtcars, mpg, times = 2, apparent = TRUE) library(purrr) resample1 < permutations(mtcars, starts_with("c"), times = 1) resample1$splits[[1]] %>% analysis() resample2 < permutations(mtcars, hp, times = 10, apparent = TRUE) map_dbl(resample2$splits, function(x) { t.test(hp ~ vs, data = analysis(x))$statistic })
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