sample_stratified: A function to create calibration and validation sample sets...

In resemble: Memory-Based Learning in Spectral Chemometrics

A function to create calibration and validation sample sets for leave-group-out cross-validation

Description

for internal use only! This is stratified sampling based on the values of a continuous response variable (y). If group is provided, the sampling is done based on the groups and the average of y per group. This function is used to create calibration and validation groups for leave-group-out cross-validations (or leave-group-of-groups-out cross-validation if group argument is provided).

Usage

sample_stratified(y, p, number, group = NULL, replacement = FALSE, seed = NULL)

Arguments

y	a matrix of one column with the response variable.
p	the percentage of samples (or groups if group argument is used) to retain in the validation_indices set
number	the number of sample groups to be crated
group	the labels for each sample in y indicating the group each observation belongs to.
replacement	A logical indicating sample replacements for the calibration set are required.
seed	an integer for random number generator (default NULL).

Value

a list with two matrices (hold_in and hold_out) giving the indices of the observations in each column. The number of columns represents the number of sampling repetitions.

resemble documentation built on May 29, 2024, 8:49 a.m.