cv.comp.reg: cross-validation for some compositional regression models

In Compositional: Compositional Data Analysis

cross-validation for some compositional regression models

Description

cross-validation for some compositional regression models.

Usage

cv.comp.reg(y, x, type = "comp.reg", nfolds = 10, folds = NULL, seed = NULL)

Arguments

y	A matrix with compositional data. Zero values are allowed for some regression models.
x	The predictor variable(s).
type	This can be one of the following: "comp.reg", "robust", "kl.compreg", "js.compreg", "diri.reg" or "zadr".
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	If seed is TRUE the results will always be the same.

Details

A k-fold cross-validation for a compositional regression model is performed.

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].

See Also

comp.reg, kl.compreg, compppr.tune, aknnreg.tune

Examples

y <- as.matrix( iris[, 1:3] )
y <- y / rowSums(y)
x <- iris[, 4]
mod <- cv.comp.reg(y, x)

Compositional documentation built on Aug. 18, 2025, 5:19 p.m.