R: predict method for locpvs objects

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 and examples for applying this predict method, `pvs` for pairwise variable selection without modeling subclasses, `predict.pvs` for predicting ‘`pvs`’-models

[Package klaR version 0.6-12 Index]