Folds: Splitting observations into folds

In sharp: Stability-enHanced Approaches using Resampling Procedures

Splitting observations into folds

Description

Generates a list of n_folds non-overlapping sets of observation IDs (folds).

Usage

Folds(data, family = NULL, n_folds = 5)

Arguments

data	vector or matrix of data. In regression, this should be the outcome data.
family	type of regression model. This argument is defined as in glmnet. Possible values include "gaussian" (linear regression), "binomial" (logistic regression), "multinomial" (multinomial regression), and "cox" (survival analysis).
n_folds	number of folds.

Details

For categorical outcomes (i.e. family argument is set to "binomial", "multinomial" or "cox"), the split is done such that the proportion of observations from each of the categories in each of the folds is representative of that of the full sample.

Value

A list of length n_folds with sets of non-overlapping observation IDs.

Examples

# Splitting into 5 folds
simul <- SimulateRegression()
ids <- Folds(data = simul$ydata)
lapply(ids, length)

# Balanced folds with respect to a binary variable
simul <- SimulateRegression(family = "binomial")
ids <- Folds(data = simul$ydata, family = "binomial")
lapply(ids, FUN = function(x) {
  table(simul$ydata[x, ])
})

sharp documentation built on April 11, 2025, 5:44 p.m.