Cross validation for the transformation-free linear regression for compositional responses and predictors {Compositional} | R Documentation |
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.
tflr, cv.olscompcomp, klalfapcr.tune
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