| Cross-validation for the naive Bayes classifiers for compositional data using the alpha-transformation {Compositional} | R Documentation |
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 = NULL)
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 |
The value of |
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 |
You can specify your own seed number here or leave it NULL. |
Details
This function estimates the performance of the naive Bayes classifier for each value of \alpha of the \alpha-transformation.
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. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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
x <- as.matrix(iris[, 1:4])
x <- x / rowSums(x)
mod <- alfanb.tune(x, ina = iris[, 5], a = c(0, 0.1, 0.2) )