rsample (version 0.0.2)

mc_cv: Monte Carlo Cross-Validation

Description

One resample of Monte Carlo cross-validation takes a random sample (without replacement) of the original data set to be used for analysis. All other data points are added to the assessment set.

Usage

mc_cv(data, prop = 3/4, times = 25, strata = NULL, ...)

Arguments

data

A data frame.

prop

The proportion of data to be retained for modeling/analysis.

times

The number of times to repeat the sampling..

strata

A variable that is used to conduct stratified sampling to create the resamples.

...

Not currently used.

Value

An tibble with classes `mc_cv`, `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.

Details

The `strata` argument causes the random sampling to be conducted *within the stratification variable*. The can help ensure that the number of data points in the analysis data is equivalent to the proportions in the original data set.

Examples

# NOT RUN {
mc_cv(mtcars, times = 2)
mc_cv(mtcars, prop = .5, times = 2)

library(purrr)
iris2 <- iris[1:130, ]

set.seed(13)
resample1 <- mc_cv(iris2, times = 3, prop = .5)
map_dbl(resample1$splits,
        function(x) {
          dat <- as.data.frame(x)$Species
          mean(dat == "virginica")
        })

set.seed(13)
resample2 <- mc_cv(iris2, strata = "Species", times = 3, prop = .5)
map_dbl(resample2$splits,
        function(x) {
          dat <- as.data.frame(x)$Species
          mean(dat == "virginica")
        })
# }