# predict.fda: Classify by Flexible Discriminant Analysis

## Description

Classify observations in conjunction with `fda`

.

## Usage

# S3 method for 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`

.

## Examples

# NOT RUN {
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
# }