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, ...)an object of class ‘locpvs’, as that created by the function “locpvs”
a data frame or matrix containing new data. If not given the same datas as used for training the ‘pvs’-model are used.
indicator (logical), whether a quick, but less accurate computation of posterior probabalities should be used or not.
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