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mda

predict.fda

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.

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

Mixture and Flexible Discriminant Analysis

v0.5-2

GPL-2

Authors

S original by Trevor Hastie & Robert Tibshirani. Original R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley. Balasubramanian Narasimhan has contributed to the upgrading of the code.

Initial release

2020-06-26