Provide starting weights for the
mda function which
performs discriminant analysis by gaussian mixtures.
mda.start(x, g, subclasses = 3, trace.mda.start = FALSE, start.method = c("kmeans", "lvq"), tries = 5, criterion = c("misclassification", "deviance"), …)
The x data, or an mda object.
The response vector g.
number of subclasses per class, as in
Show results of each iteration.
latter requires package class (from the VR package
Number of random starts.
By default, classification errors on the training data. Posterior deviance is also an option.
arguments to be passed to the mda fitter when using posterior deviance.
A list of weight matrices, one for each class.