cv.scls: Cross-validation for the SCLS model

In Compositional: Compositional Data Analysis

Cross-validation for the SCLS model

Description

Cross-validation for the SCLS model.

Usage

cv.scls(y, x, nfolds = 10, folds = NULL, seed = NULL)

Arguments

y	A matrix with compositional response data. Zero values are allowed.
x	A matrix with compositional predictors. Zero values are allowed.
nfolds	The number of folds to be used. This is taken into consideration only if the folds argument is not supplied.
folds	If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
seed	You can specify your own seed number here or leave it NULL.

Details

The function performs k-fold cross-validation for the least squares regression where the beta coefficients are constained to be positive and sum to 1.

Value

A list including:

runtime	The runtime of the cross-validation procedure.
kl	The Kullback-Leibler divergences for all runs.
js	The Jensen-Shannon divergences for all runs.
perf	The average Kullback-Leibler divergence and average Jensen-Shannon divergence.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris. M. (2024). Constrained least squares simplicial-simplicial regression. https://arxiv.org/pdf/2403.19835.pdf

See Also

scls, cv.tflr, klalfapcr.tune

Examples

library(MASS)
set.seed(1234)
y <- rdiri(214, runif(3, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- cv.scls(y, x, nfolds = 5, seed = 12345)
mod

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.