FDA {FactoMineR}R Documentation

Factorial Discriminant Analysis

Description

Performs Factorial Discriminant Analysis (FDA).

Usage

FDA(X, fact, new.data = NULL, new.fact = NULL, prior = NULL, 
    cross.val = FALSE)

Arguments

X a data frame with n rows (individuals) and p columns (including one factor).
fact a factor specifying the class for each observation.
new.data a data frame of individuals to be classified or, if new.fact is not null, which formed the test sample.
new.fact if new.dat is not null, a factor specifying the class for each observation of the test sample.
prior a vector. The prior probabilities of class membership. If unspecified, the class proportions for the training sample (new.dat) are used.
cross.val if TRUE, returns results for leave-one-out cross-validation.

Details

If there is no test sample, the evaluation of the affectation model is realised on the test sample itself.

Value

Returns a list including :

eigen.values a numeric vector containing all the eigenvalues
eigen.vectors a list of matrices containing all the eigenvectors
res.var a list of matrices containing all the results for the active variables
res.cg a list of matrices containing all the results for the centers of gravity
res.ind a list of matrices containing all the results for the individuals
call a list with the data frame and grouping factor used
df the discriminant functions
score a vector whith the individuals scores.
eval a list with all the results for the evaluation of the affectation model
res.cv a list with all the results for for leave-one-out cross-validation.

Author(s)

Jeremy Mazet jeremy.mazet@soredab.org

See Also

plot.FDA, print.FDA


[Package FactoMineR version 1.00 Index]