CDA: Classification using Canonical Discriminant Analysis

In fdm2id: Data Mining and R Programming for Beginners

Classification using Canonical Discriminant Analysis

Description

This function builds a classification model using Canonical Discriminant Analysis.

Usage

CDA(
  train,
  labels,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
tune	If true, the function returns parameters instead of a classification model.
methodparameters	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: CDA does not support reusing pre-tuned parameters.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: CDA does not produce a plot.
seed	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
...	Other parameters.

Details

The projection is computed from the class sizes, as the between-class scatter requires. The predictions, on the other hand, use equal prior probabilities – an observation goes to the nearest class centre in the canonical space, whatever the size of that class. This is the geometric reading plot.cda draws, and it is where CDA differs from LDA, which weights the classes by their observed frequencies: on an imbalanced problem the two do not predict the same thing.

Value

The classification model, as an object of class cda.

See Also

plot.cda, predict.cda, cda-class

Examples

require (datasets)
data (iris)
CDA (iris [, -5], iris [, 5])

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.