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.

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

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. This could be a single character value or a variable name that corresponds to a variable that exists in the data frame.

breaks

A single number giving the number of bins desired to stratify a numeric stratification variable.

...

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. This can help ensure that the number of data points in the analysis data is equivalent to the proportions in the original data set. (Strata below 10% of the total are pooled together.)

Examples

mc_cv(mtcars, times = 2)
#> # Monte Carlo cross-validation (0.75/0.25) with 2 resamples #> # A tibble: 2 x 2 #> splits id #> <list> <chr> #> 1 <split [24/8]> Resample1 #> 2 <split [24/8]> Resample2
mc_cv(mtcars, prop = .5, times = 2)
#> # Monte Carlo cross-validation (0.5/0.5) with 2 resamples #> # A tibble: 2 x 2 #> splits id #> <list> <chr> #> 1 <split [16/16]> Resample1 #> 2 <split [16/16]> Resample2
library(purrr) data(wa_churn, package = "modeldata") set.seed(13) resample1 <- mc_cv(wa_churn, times = 3, prop = .5) map_dbl(resample1$splits, function(x) { dat <- as.data.frame(x)$churn mean(dat == "Yes") })
#> [1] 0.2597956 0.2685974 0.2674617
set.seed(13) resample2 <- mc_cv(wa_churn, strata = "churn", times = 3, prop = .5) map_dbl(resample2$splits, function(x) { dat <- as.data.frame(x)$churn mean(dat == "Yes") })
#> [1] 0.2654742 0.2654742 0.2654742
set.seed(13) resample3 <- mc_cv(wa_churn, strata = "tenure", breaks = 6, times = 3, prop = .5) map_dbl(resample3$splits, function(x) { dat <- as.data.frame(x)$churn mean(dat == "Yes") })
#> [1] 0.2671019 0.2707919 0.2730627