R: Classify by Flexible Discriminant Analysis

predict.fda {mda} | R Documentation

## Classify by Flexible Discriminant Analysis

### Description

Classify observations in conjunction with `fda`.

### Usage

    ## S3 method for class 'fda'
    predict(object, newdata, type, prior, dimension, ...)

### Arguments

`object` | an object of class `"fda"`.
`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 (canonical) variables, `type = "posterior"` produces a matrix of posterior probabilities (based on a gaussian assumption), and `type = "hierarchical"` produces the predicted class in sequence for models of all dimensions.
`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`.
`...` | 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 `fda`. 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

`fda`, `mars`, `bruto`, `polyreg`, `softmax`, `confusion`

### Examples

    data(iris)
    irisfit <- fda(Species ~ ., data = iris)
    irisfit
    ## Call:
    ## fda(x = iris$x, g = iris$g)
    ##
    ## Dimension: 2
    ##
    ## Percent Between-Group Variance Explained:
    ##     v1  v2
    ##  99.12 100
    confusion(predict(irisfit, iris), iris$Species)
    ##            Setosa Versicolor Virginica
    ##     Setosa     50          0         0
    ## Versicolor      0         48         1
    ##  Virginica      0          2        49
    ## attr(, "error"):
    ## [1] 0.02

[Package mda version 0.4-10 Index]