R: Monte Carlo Cross-Validation

mc_cv {rsample} | R Documentation

## 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.

### 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.

### 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.

### Examples

    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")
            })

[Package rsample version 0.0.2 Index]