klaR (version 0.6-15)

locpvs: Pairwise variable selection for classification in local models

Description

Performs pairwise variable selection on subclasses.

Usage

locpvs(x, subclasses, subclass.labels, prior=NULL, method="lda", 
    vs.method = c("ks.test", "stepclass", "greedy.wilks"),
    niveau=0.05, fold=10, impr=0.1, direct="backward", out=FALSE, ...)

Arguments

x

matrix or data frame containing the explanatory variables. x must consist of numerical data only.

subclasses

vector indicating the subclasses (a factor)

subclass.labels

must be a matrix with 2 coloumns, where the first coloumn specifies the subclass and the second coloumn the according upper class

prior

prior probabilites for the classes. If not specified the prior probabilities will be set according to proportion in “subclasses”. If specified the order of prior probabilities must be the same as in “subclasses”.

method

character, name of classification function (e.g. “lda” (default)).

vs.method

character, name of variable selection method. Must be one of “ks.test” (default), “stepclass” or “greedy.wilks”.

niveau

used niveau for “ks.test

fold

parameter for cross-validation, if “stepclass” is chosen ‘vs.method

impr

least improvement of performance measure desired to include or exclude any variable (<=1), if “stepclass” is chosen ‘vs.method

direct

direction of variable selection, if “stepclass” is chosen ‘vs.method’. Must be one if “forward”, “backward” (default) or “both”.

out

indicator (logical) for textoutput during computation (slows down computation!), if “stepclass” is chosen ‘vs.method

...

further parameters passed to classification function (‘method’) or variable selection method (‘vs.method’)

Value

An object of class ‘locpvs’ containing the following components:

pvs.result

the complete output of the call to pvs (see pvs for further details

subclass.labels

the subclass.labels as specified in function call

Details

A call on pvs is performed using “subclasses” as grouping variable. See pvs for further details.

References

Szepannek, G. and Weihs, C. (2006) Local Modelling in Classification on Different Feature Subspaces. In Advances in Data Mining., ed Perner, P., LNAI 4065, pp. 226-234. Springer, Heidelberg.

See Also

predict.locpvs for predicting ‘locpvs’ models and pvs

Examples

# NOT RUN {
## this example might be a bit artificial, but it sufficiently shows how locpvs has to be used

## learn a locpvs-model on the Vehicle dataset

library("mlbench")
data("Vehicle")

subclass <- Vehicle$Class # use four car-types in dataset as subclasses
## aggregate "bus" and "van" to upper-class "big" and "saab" and "opel" to upper-class "small"
subclass_class <- matrix(c("bus","van","saab","opel","big","big","small","small"),ncol=2) 

## learn now a locpvs-model for the subclasses:
model <- locpvs(Vehicle[,1:18], subclass, subclass_class) 
model # short summary, showing the class-pairs of the submodels 
# together with the selected variables and the relation of sub- to upperclasses

## predict:
pred <- predict(model, Vehicle[,1:18])

## now you can look at the predicted classes:
pred$class
## or at the posterior probabilities:
pred$posterior
## or at the posterior probabilities for the subclasses:
pred$subclass.posteriors

# }