cv.alfahdda {CompositionalHDDA} | R Documentation |
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)
Arguments
ina |
A group indicator variable for the compositional data. |
x |
The compositional data. Zero values are allowed. |
a |
A vector of |
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. |
Details
K-fold cross-validation for the high dimensional discriminant analysis with compositional data using the \alpha
-transformation is performed.
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. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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
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)