multiCV: Cross-validation for multiple models

In twolodzko/twextras: twextras

Description

Cross-validation for multiple models

Usage

multiCV(..., data, measures = c("MAE", "RMSE", "MdAE"), weights,
  nfolds = 1L, split = 0.8)

Arguments

...	models.
data	data to be used for training and testing the models.
measures	character vector of names of error measures to be used (see err).
weights	vector of weights to be used for weighted error measures.
nfolds	number of folds for k-fold cross-validation, if nfolds < 2, it defaults to holdout sample cross-validation.
split	when usinng holdout sample cross validation (nfolds < 2), this is a fraction of data to be used as a training set.

Examples

model1 <- lm(mpg ~ 1, data = mtcars)
model2 <- lm(mpg ~ cyl + disp, data = mtcars)

multiCV(model1, model2, data = mtcars)

twolodzko/twextras documentation built on May 3, 2019, 1:52 p.m.