DFA: Discriminant Factor Analysis

In computational-metabolomics/structtoolbox: Data processing & analysis tools for Metabolomics and other omics

Discriminant Factor Analysis

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 with the following output slots:

scores	(DatasetExperiment)
loadings	(data.frame)
eigenvalues	(data.frame)
that	(DatasetExperiment)

Inheritance

A DFA object inherits the following struct classes:

⁠[DFA]⁠ >> ⁠[model]⁠ >> ⁠[struct_class]⁠

References

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

Examples

M = DFA(
      factor_name = "V1",
      number_components = 2)

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

computational-metabolomics/structtoolbox documentation built on July 2, 2024, 10:46 p.m.