pvs

0th

Percentile

Pairwise variable selection for classification

Pairwise variable selection for numerical data, allowing the use of different classifiers and different variable selection methods.

Keywords
multivariate, classif
Usage
pvs(x, ...)

## S3 method for class 'default':
pvs(x, grouping, prior=NULL, method="lda", 
    vs.method=c("ks.test","stepclass","greedy.wilks"), niveau=0.05, 
    fold=10, impr=0.1, direct="backward", out=FALSE, ...)
    
## S3 method for class 'formula':
pvs(formula, data = NULL, ...)
Arguments
x
matrix or data frame containing the explanatory variables (required, if formula is not given). x must consist of numerical data only.
formula
A formula of the form groups ~ x1 + x2 + .... That is, the response is the grouping factor (the classes) and the right hand side specifies the (numerical) discriminators. Interaction terms are not supported.
data
data matrix (rows=cases, columns=variables)
grouping
class indicator vector (a factor)
prior
prior probabilites for the classes. If not specified the prior probabilities will be set according to proportion in grouping. If specified the order of prior probabilities must be the same as in grouping.
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
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
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

The classification method (e.g. lda) must have its own predict method (like predict.lda for lda) returns a list with an element posterior containing the posterior probabilties. It must be able to deal with matrices as in method(x, grouping, ...). Examples of such classification methods are lda, qda, rda, NaiveBayes or sknn.\ For the classification methods svm and randomForest there are special routines implemented, to make them work with pvs method even though their predict methods don't provide the demanded posteriors. However those two classfiers can not be used together with variable selection method stepclass. pvs performs a variable selection using the selection method chosen in vs.method for each pair of classes in x. Then for each pair of classes a submodel using method is trained (using only the earlier selected variables for this class-pair). If method is ks.test, then for each variable the empirical distribution functions of the cases of both classes are compared via ks.test. Only variables with a p-values below niveau are used for training the submodel for this pair of classes. If method is stepclass the variable selection is performed using the stepclass method. If method is greedy.wilks the variable selection is performed using Wilk's lambda criterion.

Value

  • An object of class pvs containing the following components:
  • classesthe classes in grouping
  • priorused prior probabilities
  • methodname of used classification function
  • vs.methodname of used function for variable selection
  • submodelscontaining a list of submodels. For each pair of classes there is a list element being another list of 3 containing the class-pair of this submodel, the selected variables for the subspace of classes and the result of the trained classification function.
  • callthe (matched) function call

concept

Pairwise variable selection for classification

References

{Szepannek, G. and Weihs, C. (2006) Variable Selection for Classification of More than Two Classes Where the Data are Sparse. In From Data and Information Analysis to Kwnowledge Engineering., eds Spiliopolou, M., Kruse, R., Borgelt, C., Nuernberger, A. and Gaul, W. pp. 700-708. Springer, Heidelberg.} {Szepannek, G. (2008): Different Subspace Classification - Datenanalyse, -interpretation, -visualisierung und Vorhersage in hochdimensionalen Raeumen, ISBN 978-3-8364-6302-7, vdm, Saarbruecken.}

See Also

predict.pvs for predicting pvs models and locpvs for pairwisevariable selection in local models of several subclasses

Aliases
  • pvs
  • pvs.default
  • pvs.formula
  • print.pvs
Examples
## Example 1: learn an "lda" model on the waveform data using pairwise variable 
## selection (pvs) using "ks.test" and compare it to using lda without pvs 

library("mlbench")
trainset <- mlbench.waveform(300) 
pvsmodel <- pvs(trainset$x, trainset$classes, niveau=0.05) # default: using method="lda"
## short summary, showing the class-pairs of the submodels and the selected variables
pvsmodel
 
testset <-  mlbench.waveform(500)
## prediction of the test data set: 
prediction <- predict(pvsmodel, testset$x)

## calculating the test error rate
1-sum(testset$classes==prediction$class)/length(testset$classes)
## Bayes error is 0.149

## comparison to performance of simple lda
ldamodel <- lda(trainset$x, trainset$classes)
LDAprediction <- predict(ldamodel, testset$x)

## test error rate
1-sum(testset$classes==LDAprediction$class)/length(testset$classes)


## Example 2: learn a "qda" model with pvs on half of the Satellite dataset, 
## using "ks.test" 

library("mlbench")
data("Satellite")

model <- pvs(classes ~ ., Satellite[1:3218,], method="qda", vs.method="ks.test")
## short summary, showing the class-pairs of the submodels and the selected variables
model 

## now predict on the rest of the data set:
## pred <- predict(model,Satellite[3219:6435,]) # takes some time
pred <- predict(model,Satellite[3219:6435,], quick=TRUE) # that's much quicker

## now you can look at the predicted classes:
pred$class 
## or the posterior probabilities:
pred$posterior
Documentation reproduced from package klaR, version 0.6-11, License: GPL-2

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