DFA {structToolbox} | R Documentation |
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.
DFA(factor_name, number_components = 2, ...)
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 |
... |
Additional slots and values passed to |
A DFA
object with the following output
slots:
scores | (DatasetExperiment) |
loadings | (data.frame) |
eigenvalues | (data.frame) |
that | (DatasetExperiment) |
Manly B (1986). Multivariate Statistical Methods: A Primer. Chapman and Hall, Boca Raton.
D = iris_DatasetExperiment() M = DFA(factor_name='Species') M = model_apply(M,D)