predict.locpvs {klaR} | R Documentation |
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 ‘locpvs ’, as that created by the function “locpvs ” |
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. |
return.subclass.prediction |
indicator (logical), whether the returned object includes posterior probabilities for each date in each subclass |
... |
Further arguments are passed to underlying predict calls. |
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 “return.subclass.prediction=TRUE ”. A matrix containing posterior probabalities for the subclasses. |
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, pvs
for pairwise variable selection without modeling subclasses, predict.pvs
for predicting ‘pvs
’-models
## this example might be a bit artificial, but it sufficiently shows how locpvs has to be used ## learn a locpvs-model on the Vehicle dataset library("mlbench") data("Vehicle") subclass <- Vehicle$Class # use four car-types in dataset as subclasses ## aggregate "bus" and "van" to upper-class "big" and "saab" and "opel" to upper-class "small": subclass_class <- matrix(c("bus","van","saab","opel","big","big","small","small"), ncol=2) ## learn now a locpvs-model for the subclasses: model <- locpvs(Vehicle[,1:18], subclass, subclass_class) model # short summary, showing the class-pairs of the submodels # together with the selected variables and the relation of sub- to upperclasses ## predict: pred <- predict(model,Vehicle[,1:18]) ## now you can look at the predicted classes: pred$class ## or at the posterior probabilities: pred$posterior ## or at the posterior probabilities for the subclasses: pred$subclass.posteriors