LDA: Classification using Linear Discriminant Analysis

In fdm2id: Data Mining and R Programming for Beginners

Classification using Linear Discriminant Analysis

Description

This function builds a classification model using Linear Discriminant Analysis.

Usage

LDA(
  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: LDA does not support reusing pre-tuned parameters.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: LDA 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.

Value

The classification model.

See Also

lda

Examples

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

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