Pairwise variable selection for classification in local models
Performs pairwise variable selection on subclasses.
locpvs(x, subclasses, subclass.labels, prior=NULL, method="lda",
vs.method = c("ks.test", "stepclass", "greedy.wilks"),
niveau=0.05, fold=10, impr=0.1, direct="backward", out=FALSE, ...)x |
matrix or data frame containing the explanatory variables. x must consist of numerical data only. |
subclasses |
vector indicating the subclasses (a factor) |
subclass.labels |
must be a matrix with 2 coloumns, where the first coloumn specifies the subclass and the second coloumn the according upper class |
prior |
prior probabilites for the classes. If not specified the prior probabilities will be set according to proportion in “subclasses”. If specified the order of prior probabilities must be the same as in “subclasses”. |
method |
character, name of classification function (e.g. “ |
vs.method |
character, name of variable selection method. Must be one of “ |
niveau |
used niveau for “ |
fold |
parameter for cross-validation, if “ |
impr |
least improvement of performance measure desired to include or exclude any variable (<=1), if “ |
direct |
direction of variable selection, if “ |
out |
indicator (logical) for textoutput during computation (slows down computation!), if “ |
... |
further parameters passed to classification function (‘ |
An object of class ‘locpvs’ containing the following components:
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.
predict.locpvs for predicting ‘locpvs’ models and pvs
## 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.posteriorsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.