CompositionalHDDA (version 1.0)

cv.alfahdda: Cross-Validation of the HDDA for compositional data using the alpha-transformation

Description

Cross-Validation of the HDDA for compositional data using the alpha-transformation.

Usage

cv.alfahdda(ina, x, a = seq(-1, 1, by = 0.1), d_select = "both", 
threshold = c(0.001, 0.005, 0.05, 1:9 * 0.1), folds = NULL, stratified = TRUE, 
nfolds = 10, seed = NULL)

Value

A list including:

kl

A matrix with the configurations of hyper-parameters tested and the estimated Kullback-Leibler divergence, for each configuration.

js

A matrix with the configurations of hyper-parameters tested and the estimated Jensen-Shannon divergence, for each configuration.

Arguments

ina

A group indicator variable for the compositional data.

x

The compositional data. Zero values are allowed.

a

A vector of \(\alpha\) values.

d_select

Either "Cattell", "BIC" or "both".

threshold

A vector with numbers strictly bewtween 0 and 1. Each value corresponds to a threshold used in the Cattell's Scree-Test.

folds

If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.

stratified

Do you want the folds to be created in a stratified way? The default value is TRUE.

nfolds

The number of folds in the cross validation.

seed

You can specify your own seed number here or leave it NULL.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

K-fold cross-validation for the high dimensional discriminant analysis with compositional data using the \(\alpha\)-transformation is performed.

References

Bouveyron C. Girard S. and Schmid C. (2007). High Dimensional Discriminant Analysis. Communications in Statistics: Theory and Methods, 36(14): 2607--2623.

Bouveyron C. Celeux G. and Girard S. (2010). Intrinsic dimension estimation by maximum likelihood in probabilistic PCA. Technical Report 440372, Universite Paris 1 Pantheon-Sorbonne.

Berge L. Bouveyron C. and Girard S. (2012). HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data. Journal of Statistical Software, 46(6).

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

See Also

alfa.hdda

Examples

x <- matrix( rgamma(100 * 200, runif(200, 4, 10), 1), ncol = 200, byrow = TRUE )
x <- x / rowSums(x)  ## Dirichlet simulated values
ina <- rbinom(100, 1, 0.5)
mod <- cv.alfahdda(ina, x, a = c(0.1, 0.5, 1), d_select = "both", 
threshold = seq(0.1, 0.5, by = 0.1), nfolds = 5)