Compositional (version 5.5)

Cross validation for the transformation-free linear regression for compositional responses and predictors: Cross validation for the transformation-free linear regression for compositional responses and predictors

Description

Cross validation for the transformation-free linear regression for compositional responses and predictors.

Usage

cv.tflr(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

If seed is TRUE the results will always be the same.

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.

Details

A k-fold cross validation for the transformation-free linear regression for compositional responses and predictors is performed.

See Also

tflr, cv.olscompcomp, klalfapcr.tune

Examples

# NOT RUN {
library(MASS)
y <- rdiri(214, runif(3, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- cv.tflr(y, x)
mod
# }