Prediction of class membership and posterior probabilities using pairwise variable selection.
Usage
## S3 method for class 'pvs':
predict(object, newdata, quick = FALSE, detail = FALSE, ...)
Arguments
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.
Value
a list with components:
classthe predicted classes
posteriorposterior probabilities for the classes
details(only if details=TRUE. A list containing matrices of posterior probabalities computated by the classification method for each case and classpair.
concept
Pairwise variable selection for classification
Details
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.
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.
See Also
For more details and examples how to use this predict method, see pvs.