| predict.mda(mda) | R Documentation |
predict.mda(object, x, type, prior, dimension, ...)
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". |
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.
mda,
fda,
mars,
bruto,
polyreg,
softmax,
confusion
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)