Compositional (version 5.4)

Cross-validation for the Dirichlet discriminant analysis: Cross-validation for the Dirichlet discriminant analysis

Description

Cross-validation for the Dirichlet discriminant analysis.

Usage

cv.dda(x, ina, nfolds = 10, folds = NULL, stratified = TRUE, seed = FALSE)

Arguments

x

A matrix with the available data, the predictor variables.

ina

A vector of data. The response variable, which is categorical (factor is acceptable).

folds

A list with the indices of the folds.

nfolds

The number of folds to be used. This is taken into consideration only if "folds" is NULL.

stratified

Do you want the folds to be selected using stratified random sampling? This preserves the analogy of the samples of each group. Make this TRUE if you wish.

seed

If you set this to TRUE, the same folds will be created every time.

Value

A list including:

percent

The percentage of correct classification

runtime

The duration of the cross-validation proecdure.

Details

This function estimates the performance of the Dirichlet discriminant analysis via k-fold cross-validation.

References

Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.

Thomas P. Minka (2003). Estimating a Dirichlet distribution. http://research.microsoft.com/en-us/um/people/minka/papers/dirichlet/minka-dirichlet.pdf

See Also

dda, alfanb.tune, alfarda.tune, compknn.tune, cv.compnb

Examples

# NOT RUN {
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
mod <- cv.dda(x, ina = iris[, 5] )
# }