# predict.mda

0th

Percentile

##### Classify by Mixture Discriminant Analysis

Classify observations in conjunction with mda.

Keywords
classif
##### Usage
# S3 method for 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.

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

• predict.mda
##### Examples
# NOT RUN {
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)
# }

Documentation reproduced from package mda, version 0.4-10, License: GPL-2

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