| Cross-validation for the SCLS model {Compositional} | R Documentation |
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 mtsagris@uoc.gr.
References
Tsagris. M. (2024). Constrained least squares simplicial-simplicial regression. https://arxiv.org/pdf/2403.19835.pdf
See Also
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