This function cross-validates regression models in a user-supplied new sample or by using holdout (train-test), k-fold, or leave-one-out cross-validation.

## Usage

performance_cv(
model,
data = NULL,
method = c("holdout", "k_fold", "loo"),
metrics = "all",
prop = 0.3,
k = 5,
stack = TRUE,
verbose = TRUE,
...
)

## Arguments

model

A regression model.

data

Optional. A data frame containing the same variables as model that will be used as the cross-validation sample.

method

Character string, indicating the cross-validation method to use: whether holdout ("holdout", aka train-test), k-fold ("k_fold"), or leave-one-out ("loo"). If data is supplied, this argument is ignored.

metrics

Can be "all", "common" or a character vector of metrics to be computed (some of c("ELPD", "Deviance", "MSE", "RMSE", "R2")). "common" will compute R2 and RMSE.

prop

If method = "holdout", what proportion of the sample to hold out as the test sample?

k

If method = "k_fold", the number of folds to use.

stack

Logical. If method = "k_fold", should performance be computed by stacking residuals from each holdout fold and calculating each metric on the stacked data (TRUE, default) or should performance be computed by calculating metrics within each holdout fold and averaging performance across each fold (FALSE)?

verbose

Toggle warnings.

...

Not used.

## Value

A data frame with columns for each metric requested, as well as k

if method = "holdout" and the Method used for cross-validation. If method = "holdout" and stack = TRUE, the standard error (standard deviation across holdout folds) for each metric is also included.

## Examples

model <- lm(mpg ~ wt + cyl, data = mtcars)
performance_cv(model)
#> # Cross-validation performance (30% holdout method)
#>
#> MSE | RMSE |   R2
#> -----------------
#> 6.9 |  2.6 | 0.83