predict method for locpvs objects
Prediction of class membership and posterior probabilities in local models using pairwise variable selection.
## S3 method for class 'locpvs' predict(object,newdata, quick = FALSE, return.subclass.prediction = TRUE, ...)
object |
an object of class ‘ |
newdata |
a data frame or matrix containing new data. If not given the same datas as used for training the ‘ |
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 |
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.
a list with components:
class |
the predicted (upper) classes |
posterior |
posterior probabilities for the (upper) classes |
subclass.posteriors |
(only if “ |
Gero Szepannek, szepannek@statistik.tu-dortmund.de, Christian Neumann
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
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