flexdaCMA-methods: Flexible Discriminant Analysis

In CMA: Synthesis of microarray-based classification

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.

CMA documentation built on Nov. 8, 2020, 5:02 p.m.