predict.fda: Classify by Flexible Discriminant Analysis

In mda: Mixture and Flexible Discriminant Analysis

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

mda documentation built on July 9, 2023, 7:14 p.m.