R: Pairwise variable selection for classification in local...

locpvs {klaR} | R Documentation

## 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`’)

### Details

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

### 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

### Author(s)

Gero Szepannek, szepannek@statistik.tu-dortmund.de, Christian Neumann

### 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

    ## 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

[Package klaR version 0.6-12 Index]