flexdaCMA-methods {CMA} | R Documentation

## Flexible Discriminant Analysis

### Description

This method is experimental.

It is easy to show that, after appropriate scaling of the predictor matrix `X`, Fisher's Linear Discriminant Analysis is equivalent to Discriminant Analysis in the space of the fitted values from the linear regression of the `nlearn x K` indicator matrix of the class labels on `X`. This gives rise to 'nonlinear discrimant analysis' methods that expand `X` in a suitable, more flexible basis. In order to avoid overfitting, penalization is used. In the implemented version, the linear model is replaced by a generalized additive one, using the package `mgcv`.

### Methods

X = "matrix", y = "numeric", f = "missing"
  signature 1
X = "matrix", y = "factor", f = "missing"
  signature 2
X = "data.frame", y = "missing", f = "formula"
  signature 3
X = "ExpressionSet", y = "character", f = "missing"
  signature 4

For further argument and output information, consult `flexdaCMA`.

[Package CMA version 1.2.0 Index]