DFA {structToolbox} | R Documentation

## 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)

### 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)

[Package structToolbox version 1.6.1 Index]