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

```
# S3 method for pvs
predict(object, newdata, quick = FALSE, detail = FALSE, ...)
```

object

an object of class ‘`pvs`

’, as that created by the function “`pvs`

”

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.

detail

indicator (logical), whether the returned object includes additional information about the posterior probabilities for each date in each submodel.

…

Further arguments are passed to underlying `predict`

calls.

a list with components:

the predicted classes

posterior probabilities for the classes

(only if “`details=TRUE`

”. A list containing matrices of posterior probabalities computated by the classification method for each case and classpair.

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) 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.

For more details and examples how to use this predict method, see `pvs`

.