| Cross validation for the TFLR model {Compositional} | R Documentation |
Cross validation for the TFLR model
Description
Cross validation for the TFLR model.
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. |
Details
A k-fold cross validation for the transformation-free linear regression for compositional responses and predictors 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 mtsagris@uoc.gr.
References
Fiksel J., Zeger S. and Datta A. (2022). A transformation-free linear regression for compositional outcomes and predictors. Biometrics, 78(3): 974–987.
Tsagris. M. (2024). Constrained least squares simplicial-simplicial regression. https://arxiv.org/pdf/2403.19835.pdf
See Also
Examples
library(MASS)
y <- rdiri(100, runif(3, 1, 3))
x <- as.matrix(fgl[1:100, 2:9])
x <- x / rowSums(x)
mod <- cv.tflr(y, x)
mod