R: Classify by Mixture Discriminant Analysis

predict.mda {mda} | R Documentation

## 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)

[Package mda version 0.5-2 Index]