Cross-validation for the Dirichlet discriminant analysis {Compositional} | R Documentation |
Cross-validation for the Dirichlet discriminant analysis.
cv.dda(x, ina, nfolds = 10, folds = NULL, stratified = TRUE, seed = FALSE)
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. |
This function estimates the performance of the Dirichlet discriminant analysis via k-fold cross-validation.
A list including:
percent |
The percentage of correct classification |
runtime |
The duration of the cross-validation proecdure. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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
dda, alfanb.tune, alfarda.tune, compknn.tune, cv.compnb
x <- as.matrix(iris[, 1:4]) x <- x / rowSums(x) mod <- cv.dda(x, ina = iris[, 5] )