Compositional (version 3.5)

Cross validation for the regularised discriminant analysis with compositional data using the alpha-transformation: Cross validation for the regularised discriminant analysis with compositional data using the \(\alpha\)-transformation

Description

Cross validation for the regularised discriminant analysis with compositional data using the \(\alpha\)-transformation.

Usage

alfarda.tune(x, ina, a = seq(-1, 1, by = 0.1), nfolds = 10,
gam = seq(0, 1, by = 0.1), del = seq(0, 1, by = 0.1),
ncores = 1, folds = NULL, stratified = TRUE, seed = FALSE)

Arguments

x

A matrix with the available compositional data. Zeros are allowed.

ina

A group indicator variable for the avaiable data.

a

A vector with a grid of values 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.

nfolds

The number of folds. Set to 10 by default.

gam

A vector of values between 0 and 1. It is the weight of the pooled covariance and the diagonal matrix.

del

A vector of values between 0 and 1. It is the weight of the LDA and QDA.

ncores

The number of cores to use. If it is more than 1 parallel computing is performed. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process.

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? TRUE or FALSE.

seed

If seed is TRUE the results will always be the same.

Value

A list including:

res

The estimated optimal rate and the best values of \(\alpha\), \(gamma\) and \(delta\).

percent

For the best value of \(\alpha\) the averaged over all folds best prates of correct classification. It is a matrix, where rows correspond to the \(\gamma\) values and columns correspond to \(\delta\) values.

se

The estimated standard errors of the "percent" matrix.

runtime

The runtime of the cross-validation procedure.

Details

A k-fold cross validation is performed.

References

Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin

Tsagris M.T., Preston S. and Wood A.T.A. (2016). Improved classification for compositional data using the \(\alpha\)-transformation.

See Also

alfa.rda, rda.tune

Examples

# NOT RUN {
library(MASS)
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
ina <- fgl[, 10]
moda <- alfarda.tune(x, ina, a = seq(0.7, 1, by = 0.1), nfolds = 10,
gam = seq(0.1, 0.3, by = 0.1), del = seq(0.1, 0.3, by = 0.1) )
# }