predict.pvs: predict method for pvs objects

In klaR: Classification and Visualization

predict method for pvs objects

Description

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.

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.

Value

a list with components:

class	the predicted classes
posterior	posterior 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.

Author(s)

Gero Szepannek, [e-mail], Christian Neumann

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.

klaR documentation built on March 31, 2023, 7:19 p.m.