R: Generate cross-validated test-training pairs

crossv_mc {modelr} | R Documentation

## Generate cross-validated test-training pairs

### Description

`crossv_kfold` splits the data into `k` exclusive partitions, and uses each partition for a test-training split. `crossv_mc` generates `n` random partitions, holding out `p` of the data for training.

### Usage

    crossv_mc(data, n, test = 0.2, id = ".id")

    crossv_kfold(data, k = 5, id = ".id")

### Arguments

`data` | A data frame
`n` | Number of test-training pairs to generate (an integer).
`test` | Proportion of observations that should be held out for testing (a double).
`id` | Name of variable that gives each model a unique integer id.
`k` | Number of folds (an integer).

### Value

A data frame with `n`/`k` rows and columns `test` and `train`. `test` and `train` are list-columns containing `resample` objects.

### Examples

    cv1 <- crossv_kfold(mtcars, 5)
    cv1

    library(purrr)
    cv2 <- crossv_mc(mtcars, 100)
    models <- map(cv2$train, ~ lm(mpg ~ wt, data = .))
    errs <- map2_dbl(models, cv2$test, rmse)
    hist(errs)

[Package modelr version 0.1.1 Index]