klalfapcr.tune: Tuning of the divergence based regression for compositional...

In Compositional: Compositional Data Analysis

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 [e-mail] and Michail Tsagris [e-mail].

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

See Also

kl.alfapcr, cv.tflr, glm.pcr, alfapcr.tune

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

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.