predict.mda {mda}R Documentation

Classify by Mixture Discriminant Analysis

Description

Classify observations in conjunction with mda.

Usage

predict.mda(object, x, type, prior, dimension, ...)

Arguments

object a fitted mda object.
x new data at which to make predictions. If missing, the training data is used.
type kind of predictions: type = "class" (default) produces a fitted factor, type = "variates" produces a matrix of discriminant variables (note that the maximal dimension is determined by the number of subclasses), type = "posterior" produces a matrix of posterior probabilities (based on a gaussian assumption), type = "hierarchical" produces the predicted class in sequence for models of dimensions specified by dimension argument.
prior the prior probabability vector for each class; the default is the training sample proportions.
dimension the dimension of the space to be used, no larger than the dimension component of object, and in general less than the number of subclasses. dimension can be a vector for use with type = "hierarchical".

Value

An appropriate object depending on type. object has a component fit which is regression fit produced by the method argument to mda. There should be a predict method for this object which is invoked. This method should itself take as input object and optionally x.

See Also

mda, fda, mars, bruto, polyreg, softmax, confusion

Examples

data(glass)
samp <- sample(1:nrow(glass), 100)
glass.train <- glass[samp,]
glass.test <- glass[-samp,]
glass.mda <- mda(Type ~ ., data = glass.train)
predict(glass.mda, glass.test, type = "post") # abbreviations are allowed
confusion(glass.mda, glass.test)