predict.locpvs {klaR}R Documentation

predict method for locpvs objects

Description

Prediction of class membership and posterior probabilities in local models using pairwise variable selection.

Usage

## S3 method for class 'locpvs':
predict(object,newdata, quick = FALSE, return.subclass.prediction = TRUE, ...)

Arguments

object an object of class ‘locpvs’, as that created by the function “locpvs
newdata a data frame or matrix containing new data. If not given the same datas as used for training the ‘pvs’-model are used.
quick indicator (logical), whether a quick, but less accurate computation of posterior probabalities should be used or not.
return.subclass.prediction indicator (logical), whether the returned object includes posterior probabilities for each date in each subclass
... Further arguments are passed to underlying predict calls.

Details

Posterior probabilities are predicted as if object is a standard ‘pvs’-model with the subclasses as classes. Then the posterior probabalities are summed over all subclasses for each class. The class with the highest value becomes the prediction.

If “quick=FALSE” the posterior probabilites for each case are computed using the pairwise coupling algorithm presented by Hastie, Tibshirani (1998). If “quick=FALSE” a much quicker solution is used, which leads to less accurate posterior probabalities. In almost all cases it doesn't has a negative effect on the classification result.

Value

a list with components:

class the predicted (upper) classes
posterior posterior probabilities for the (upper) classes
subclass.posteriors (only if “return.subclass.prediction=TRUE”. A matrix containing posterior probabalities for the subclasses.

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

locpvs for learning ‘locpvs’-models, pvs for pairwise variable selection without modeling subclasses, predict.pvs for predicting ‘pvs’-models

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 0.5-8 Index]