R: Tuning of the divergence based regression for compositional...

Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the alpha-transformation {Compositional}

R Documentation

Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the `\alpha`-transformation

Description

Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the \alpha-transformation.

Usage

klalfapcr.tune(y, x, covar = NULL, nfolds = 10, maxk = 50, a = seq(-1, 1, by = 0.1),
folds = NULL, graph = FALSE, tol = 1e-07, maxiters = 50, seed = NULL)

Arguments

`y`	A numerical matrix with compositional data with or without zeros.
`x`	A matrix with the predictor variables, the compositional data. Zero values are allowed.
`covar`	If you have other continuous covariates put themn here.
`nfolds`	The number of folds for the K-fold cross validation, set to 10 by default.
`maxk`	The maximum number of principal components to check.
`a`	The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If `\alpha=0` the isometric log-ratio transformation is applied.
`folds`	If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
`graph`	If graph is TRUE (default value) a plot will appear.
`tol`	The tolerance value to terminate the Newton-Raphson procedure.
`maxiters`	The maximum number of Newton-Raphson iterations.
`seed`	You can specify your own seed number here or leave it NULL.

Details

The M-fold cross validation is performed in order to select the optimal values for \alpha and k, the number of principal components. The \alpha-transformation is applied to the compositional data first, the first k principal component scores are calcualted and used as predictor variables for the Kullback-Leibler divergence based regression model. This procedure is performed M times during the M-fold cross validation.

Value

A list including:

`mspe`	A list with the KL divergence for each value of `\alpha` and k in every fold.
`performance`	A matrix with the KL divergence for each value of `\alpha` averaged over all folds. If graph is set to TRUE this matrix is plotted.
`best.perf`	The minimum KL divergence.
`params`	The values of `\alpha` and k corresponding to the minimum KL divergence.

Author(s)

Initial code by Abdulaziz Alenazi. Modifications by Michail Tsagris.

R implementation and documentation: Abdulaziz Alenazi a.alenazi@nbu.edu.sa and Michail Tsagris mtsagris@uoc.gr.

References

Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219–238. https://jds-online.org/journal/JDS/article/136/file/pdf

Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47–57. http://arxiv.org/pdf/1508.01913v1.pdf

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. http://arxiv.org/pdf/1106.1451.pdf

Examples

library(MASS)
y <- rdiri( 214, runif(4, 1, 3) )
x <- as.matrix( fgl[, 2:9] )
x <- x / rowSums(x)
mod <- klalfapcr.tune(y = y, x = x, a = c(0.7, 0.8) )
mod

[Package Compositional version 6.9 Index]

Tuning of the divergence based regression for compositional data with compositional data in the covariates side using the \alpha-transformation