Cross-validation for the naive Bayes classifiers for compositional data {Compositional} | R Documentation |
Cross-validation for the naive Bayes classifiers for compositional data.
cv.compnb(x, ina, type = "beta", folds = NULL, nfolds = 10, stratified = TRUE, seed = FALSE, pred.ret = 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). |
type |
The type of naive Bayes, "beta", "logitnorm", "cauchy", "laplace", "gamma", "normlog" or "weibull". For the last 4 distributions, the negative of the logarithm of the compositional data is applied first. |
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. |
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. |
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.
x <- as.matrix(iris[, 1:4]) x <- x / rowSums(x) mod <- cv.compnb(x, ina = iris[, 5] )