Cross-validation for the maximum likelihood discriminant analysis using the multivariate Cauchy distribution {mvcauchy}

Cross-validation for the maximum likelihood discriminant analysis using the multivariate Cauchy distribution

Description

Cross-validation for the maximum likelihood discriminant analysis using the multivariate Cauchy distribution.

Usage

cv.cauchynb(x, ina, folds = NULL, nfolds = 10,
            stratified = TRUE, seed = FALSE, pred.ret = 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.

pred.ret

If you want the predicted values returned set this to TRUE.

Value

A list including:

preds

If pred.ret is TRUE the predicted values for each fold are returned as elements in a list.

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. If you want to compute other metrics of accuracy we suggest you choose "pred.ret = TRUE" when running the function and then write a simple function to compute more metrics, offered in the package Rfast2.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

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

See Also

cauchynb.pred, cauchy.nb

Examples

x <- as.matrix(iris[, 1:4])
mod <- cv.cauchynb(x = x, ina = iris[, 5])