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Cross-validation for the Cauchy discriminant analysis.
cv.mvcauchyda(x, ina, nfolds = 10, folds = NULL, stratified = TRUE, seed = NULL)A list including:
The estimated rate of correct classification.
The time required by the cross-validation procedure.
A matrix with the data.
A group indicator variable for the avaiable data.
The number of folds in the cross validation.
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
Do you want the folds to be created in a stratified way? TRUE or FALSE.
You can specify your own seed number here or leave it NULL.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Cross validation is performed to select the optimal parameters for the maximum likelihood Cauchy discriminant analysis and also estimate the rate of accuracy.
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2017). The elements of statistical learning. New York: Springer.
mvcauchy.da
mod <- cv.mvcauchyda(as.matrix(iris[, 1:4]), iris[, 5])
mod
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