Cross-validation for the robust quadratic discriminant analysis {robqda} | R Documentation |
Cross-validation for the robust quadratic discriminant analysis
Description
Cross-validation for the robust quadratic discriminant analysis.
Usage
robqda.cv(x, ina, nfolds = 10, quantile.used = floor((n + p + 1)/2),
nsamp = "best", folds = NULL, stratified = TRUE, seed = NULL)
Arguments
x |
A matrix with the data. |
ina |
A group indicator variable for the avaiable data. |
nfolds |
The number of folds in the cross validation. |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
quantile.used |
A number, the minimum number of the data points regarded as good points. |
nsamp |
The number of samples or "best", "exact", or "sample". The limit If "sample" the number chosen is min(5 * p, 3000), taken from Rousseeuw and Hubert (1997). If "best" exhaustive enumeration is done up to 5000 samples: if "exact" exhaustive enumeration will be attempted. |
stratified |
Do you want the folds to be created in a stratified way? TRUE or FALSE. |
seed |
You can specify your own seed number here or leave it NULL. |
Details
Cross validation is performed to estimate the rate of accuracy in the robust quadratic discriminant analysis.
Value
A list including:
per |
A vector with the estimated rate of correct classification for every fold. |
percent |
A matrix with the mean estimated rates of correct classification. |
runtime |
The time required by the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.
See Also
Examples
x <- as.matrix(iris[, 1:4]) + matrix(rnorm(150 * 4), ncol = 4 )
mod <- robqda.cv(x, iris[, 5], nfolds = 5)
mod