Compositional (version 5.4)

Cross-validation for the naive Bayes classifiers for compositional data using the alpha-transformation: Cross-validation for the naive Bayes classifiers for compositional data using the \(\alpha\)-transformation

Description

Cross-validation for the naive Bayes classifiers for compositional data using the \(\alpha\)-transformation.

Usage

alfanb.tune(x, ina, a = seq(-1, 1, by = 0.1), type = "gaussian",
folds = NULL, nfolds = 10, 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).

a

This can be a vector of values or a single number.

type

The type of naive Bayes, "gaussian", "cauchy" or "laplace".

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:

crit

A vector whose length is equal to the number of k and is the accuracy metric for each k. For the classification case it is the percentage of correct classification.

Details

This function estimates the performance of the naive Bayes classifier for each value of \(\alpha\) of the \(\alpha\)-transformation.

References

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

See Also

alfa.nb, alfarda.tune, compknn.tune, cv.dda, cv.compnb

Examples

# NOT RUN {
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
mod <- alfanb.tune(x, ina = iris[, 5], a = c(0, 0.1, 0.2) )
# }