## 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
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