rsample (version 0.1.0)

group_vfold_cv: Group V-Fold Cross-Validation

Description

Group V-fold cross-validation creates splits of the data based on some grouping variable (which may have more than a single row associated with it). The function can create as many splits as there are unique values of the grouping variable or it can create a smaller set of splits where more than one value is left out at a time.

Usage

group_vfold_cv(data, group = NULL, v = NULL, ...)

Arguments

data

A data frame.

group

This could be a single character value or a variable name that corresponds to a variable that exists in the data frame.

v

The number of partitions of the data set. If let NULL, v will be set to the number of unique values in the group.

...

Not currently used.

Value

A tibble with classes group_vfold_cv, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and an identification variable.

Examples

# NOT RUN {
set.seed(3527)
test_data <- data.frame(id = sort(sample(1:20, size = 80, replace = TRUE)))
test_data$dat <- runif(nrow(test_data))

set.seed(5144)
split_by_id <- group_vfold_cv(test_data, group = "id")

get_id_left_out <- function(x)
  unique(assessment(x)$id)

library(purrr)
table(map_int(split_by_id$splits, get_id_left_out))

set.seed(5144)
split_by_some_id <- group_vfold_cv(test_data, group = "id", v = 7)
held_out <- map(split_by_some_id$splits, get_id_left_out)
table(unlist(held_out))
# number held out per resample:
map_int(held_out, length)
# }