resamplr: Resampling methods

# Example originally from modelr::crossv_mc
library("purrr")
library("dplyr")

# holdout three obs, repeat 10 times
cv1 <- holdout_n(mtcars, size = 3, K = 10)
models <- map(cv1$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv1$test, modelr::rmse))

# holdout two groups at a time in the test set
# repeat four times.
cv2 <- holdout_n(group_by(mtcars, cyl), size = 2, K = 4)
models <- map(cv2$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv2$test, modelr::rmse))

# stratified holdout
# holdout 1 obs each from each group. repeat 5 times.
cv3 <- holdout_n(group_by(mtcars, am), size = 1, K = 5, stratified = TRUE)
models <- map(cv3$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv3$test, modelr::rmse))

# Holdout fraction of the data

# holdout 30% of observations, repeat 10 times
cv4 <- holdout_frac(mtcars, size = 0.3, K = 10)
models <- map(cv4$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv4$test, modelr::rmse))

# holdout 30% of groups at a time in the test set
cv5 <- holdout_frac(group_by(mtcars, cyl), size = 0.3, K = 10)
models <- map(cv5$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv5$test, modelr::rmse))

# stratified holdout
# holdout 30% of obs within each group.
cv6 <- holdout_frac(group_by(mtcars, am), size = 0.3, K = 10, stratified = TRUE)
models <- map(cv6$train, ~ lm(mpg ~ wt, data = .))
summary(map2_dbl(models, cv6$test, modelr::rmse))