Cross-validation for the constrained linear least squares for compositional responses and predictors {Compositional} R Documentation

## Cross-validation for the constrained linear least squares for compositional responses and predictors

### Description

Cross-validation for the constrained linear least squares for compositional responses and predictors.

### Usage

```cv.olscompcomp(y, x, rs = 5, tol = 1e-4, nfolds = 10, folds = NULL, seed = FALSE)
```

### Arguments

 `y` A matrix with compositional response data. Zero values are allowed. `x` A matrix with compositional predictors. Zero values are allowed. `rs` The number of times to run the constrained optimisation using different random starting values each time. `tol` The threshold upon which to stop the iterations of the constrained optimisation. `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

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.

`ols.compcomp, cv.tflr, klalfapcr.tune `

### 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.olscompcomp(y, x, rs = 1, tol = 1e-4, nfolds = 5, folds = NULL, seed = 12345)
mod

```

[Package Compositional version 5.2 Index]