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Tuning the value of
alfareg.tune(y, x, a = seq(0.1, 1, by = 0.1), nfolds = 10,
folds = NULL, nc = 1, seed = NULL, graph = FALSE)A matrix with compositional data. zero values are allowed.
A matrix with the continuous predictor variables or a data frame including categorical predictor variables.
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
The number of folds to split the data.
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
The number of cores to use. IF you have a multicore computer it is advisable to use more than 1. It makes the procedure faster. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process.
You can specify your own seed number here or leave it NULL.
If graph is TRUE a plot of the performance for each fold along the values of
A plot of the estimated Kullback-Leibler divergences (multiplied by 2) along the values of
The runtime required by the cross-validation.
A matrix with twice the Kullback-Leibler divergence of the observed from the fitted values. Each row corresponds to a fold and each column to a value of
A vector with twice the Kullback-Leibler divergence of the observed from the fitted values. Every value corresponds to a value of
The optimal value of
The minimum value of twice the Kullback-Leibler.
The
Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://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. https://arxiv.org/pdf/1106.1451.pdf
# NOT RUN {
library(MASS)
y <- as.matrix(fgl[1:40, 2:4])
y <- y /rowSums(y)
x <- as.vector(fgl[1:40, 1])
mod <- alfareg.tune(y, x, a = seq(0, 1, by = 0.1), nfolds = 5)
# }
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