DFA: Discriminant Factor Analysis

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes.

Usage

DFA(factor_name, number_components = 2, ...)

Arguments

factor_name	(character) The name of a sample-meta column to use.
number_components	(numeric, integer) The number of DFA components calculated. The default is 2.
...	Additional slots and values passed to struct_class.

Value

A DFA object.

References

Manly B (1986). Multivariate Statistical Methods: A Primer. Chapman and Hall, Boca Raton.

Examples

D = iris_DatasetExperiment()
M = DFA(factor_name='Species')
M = model_apply(M,D)

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.