xtractvars: Variable clustering based variable selection

In klaR: Classification and Visualization

Variable clustering based variable selection

Description

Applies variable selection to data based on variable clusterings as resulting from corclust or CLV.

Usage

xtractvars(object, data, thres = 0.5)

Arguments

object	Object of class cvtree applied to a corclust object or the summary() of a clv object as created by CLV.
data	Data where variables are to be selected. Coloumn names must be identical to those used in corclust model.
thres	Maximum accepted average within cluster correlation for selection of a variable.

Details

Of each cluster the first variable is selected as well as all other variables with an average within cluster correlation below thres.

Value

The data is returned where unselected coloumns are removed.

Author(s)

Gero Szepannek

References

Roever, C. and Szepannek, G. (2005): Application of a genetic algorithm to variable selection in fuzzy clustering. In C. Weihs and W. Gaul (eds), Classification - The Ubiquitous Challenge, 674-681, Springer.

See Also

See also corclust, cvtree and CLV.

Examples

    data(B3)
    ccres <- corclust(B3)
    plot(ccres)
    cvtres <- cvtree(ccres, k = 3)
    newdata <- xtractvars(cvtres, B3, thres = 0.5) 

klaR documentation built on March 31, 2023, 3:03 p.m.