# predict.mda

##### 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`

.

##### See Also

##### 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*