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

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

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.

a list with components:

the predicted (upper) classes

posterior probabilities for the (upper) classes

(only if “`return.subclass.prediction=TRUE`

”. A matrix containing posterior probabalities for the subclasses.

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.

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.

`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