How the folds are drawn — cv • timesift

The resampling timesift() scores on. cv() deals units into folds balanced on the stratifying value; grouped_cv() deals whole groups, keeping every target sharing a group value on one side of each split. block_cv() and env_cv() hold out a block of units whole: a region of space for the first, a region of predictor space for the second.

Usage

cv(v = 10L, seed = 1L, strata = 5L, by = NULL, repeats = 1L)

block_cv(by, v = 4L)

env_cv(by, v = 4L)

grouped_cv(group, v = 10L, seed = 1L, repeats = 1L)

Arguments

v: Number of folds.
seed: Random seed, fixed so the map is reproducible.
strata: Number of strata, or 1 for no stratification.
by: What cv() stratifies on instead of the richness of the response: the name of a numeric column of targets, or a vector with one value per target. For block_cv() and env_cv(), the columns the blocks are cut on: names of columns of targets, or a numeric matrix with one row per target.
repeats: Number of times the split is drawn, each with its own seed (seed, seed + 1, and so on). In timesift() every repeat is a full run on its own fold map: a score stays one fit's held-out score, the report averages a response's scores over folds and repeats, and the combiner is fitted on the out-of-fold predictions of all the repeats together. block_cv() and env_cv() draw nothing, so repeat nothing.
group: The grouping: the name of a column of targets, or a vector with one value per target.

Value

A timesift_resampling.

Details

The blocks are cut by halving. The units are split at the median of the column with the widest range (the first on a tie), the lower share going to the left and the units tied on that column ordered as they arrived, and each part is cut again until there are v of them, so a block holds as many units as another to within one and a map of v blocks has v folds. Nothing is drawn, so both languages return the same map. env_cv() centres and scales every column first, which a coordinate system does not need. Under the nested selection the inner folds keep blocks whole as well, so n_inner is at most v - 1.

resampling also accepts a fold vector or a fold_map() result directly, which is how a split the package has no constructor for – a spatial block, a season held out whole – reaches the same fitting path.

Examples

cv(v = 5L)
cv(v = 5L, by = "elevation")
grouped_cv("site")
block_cv(c("x", "y"), v = 4L)
env_cv(c("elevation", "slope"), v = 5L)