# predict.fda

##### Classify by Flexible Discriminant Analysis

Classify observations in conjunction with `fda`

.

- Keywords
- classif

##### 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`

.

##### See Also

##### 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
# }
```

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