predict.mda: Classify by Mixture Discriminant Analysis

In mda: Mixture and Flexible Discriminant Analysis

Classify by Mixture Discriminant Analysis

Description

Classify observations in conjunction with mda.

Usage

## S3 method for class 'mda'
predict(object, newdata, type, prior, dimension, g, ...)

Arguments

object	a fitted mda object.
newdata	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 probability 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".
g	???
...	further arguments to be passed to or from methods.

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 newdata.

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)

mda documentation built on July 9, 2023, 7:14 p.m.