Compositional: cv.tflr – R documentation

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cv.tflr

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.

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

`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.

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

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.

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

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

Compositional Data Analysis

v4.6

GPL (>= 2)

Authors

Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]

Initial release

2021-04-27