Discriminant Factor Analysis — DFA • structToolbox

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)