Cross-validation for the Dirichlet discriminant analysis.
cv.dda(x, ina, nfolds = 10, folds = NULL, stratified = TRUE, seed = FALSE)A matrix with the available data, the predictor variables.
A vector of data. The response variable, which is categorical (factor is acceptable).
A list with the indices of the folds.
The number of folds to be used. This is taken into consideration only if "folds" is NULL.
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.
If you set this to TRUE, the same folds will be created every time.
A list including:
The percentage of correct classification
The duration of the cross-validation proecdure.
This function estimates the performance of the Dirichlet discriminant analysis via k-fold cross-validation.
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
# NOT RUN {
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
mod <- cv.dda(x, ina = iris[, 5] )
# }
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