datasplit: Split data for k-fold cross-validation

In spm2: Spatial Predictive Modeling

Split data for k-fold cross-validation

Description

This function is a data splitting function for k-fold cross- validation and uses a stratified random sampling technique. It resamples the training data based on sample quantiles.

Usage

datasplit(trainy, k.fold = 10)

Arguments

trainy	a vector of response, must have a length equal to sample size.
k.fold	integer; number of folds in the cross-validation. if > 1, then apply k-fold cross validation; the default is 10, i.e., 10-fold cross validation that is recommended.

Value

A list of samples each with an index of k-fold number.

Note

This function is largely based on rfcv in randomForest.

Author(s)

Jin Li

References

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Examples

library(spm)
data(petrel)
idx1 <- datasplit(petrel[, 3], k.fold = 10)
table(idx1)

spm2 documentation built on April 6, 2023, 5:19 p.m.