predict.mda {mda}R Documentation

make predictions from an mda object

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 < R, 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)


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