robqda (version 1.0)

Robust quadratic discriminant analysis: Robust quadratic discriminant analysis

Description

Robust quadratic discriminant analysis.

Usage

robqda(xnew, x, ina, quantile.used = floor((n + p + 1)/2), nsamp = "best")

Value

A list including:

mesos

The estimated robust multivariate locations.

sk

The estimated robust scatter matrices.

est

The estimated group membership of the new data.

Arguments

xnew

A matrix with the new data whose group is to be predicted. They have to be continuous.

x

A matrix with the available data. They have to be continuous.

ina

A group indicator variable for the avaiable data.

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.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

Robust quadratic discriminant analysis is performed where robust estimates (MCD method) of the multivariate location and scatter are used.

References

P. Rousseeuw and M. Hubert (1997) Recent developments in PROGRESS. In L1-Statistical Procedures and Related Topics ed Y. Dodge, IMS Lecture Notes volume 31, pp. 201--214.

Friedman Jerome, Trevor Hastie and Robert Tibshirani (2009). The elements of statistical learning, 2nd edition. Springer, Berlin.

See Also

robqda.cv

Examples

x <- as.matrix(iris[, 1:4]) + matrix(rnorm(150 * 4), ncol = 4 )
ina <- iris[, 5]
mod <- robqda(x, x, ina)
table(ina, mod$est)