MDAModel: Mixture Discriminant Analysis Model

Description

Performs mixture discriminant analysis.

Usage

MDAModel(
  subclasses = 3,
  sub.df = numeric(),
  tot.df = numeric(),
  dimension = sum(subclasses) - 1,
  eps = .Machine$double.eps,
  iter = 5,
  method = .(mda::polyreg),
  trace = FALSE,
  ...
)

Value

MLModel class object.

Arguments

subclasses: numeric value or vector of subclasses per class.
sub.df: effective degrees of freedom of the centroids per class if subclass centroid shrinkage is performed.
tot.df: specification of the total degrees of freedom as an alternative to sub.df.
dimension: dimension of the discriminant subspace to use for prediction.
eps: numeric threshold for automatically truncating the dimension.
iter: limit on the total number of iterations.
method: regression function used in optimal scaling. The default of linear regression is provided by polyreg from the mda package. For penalized mixture discriminant models, gen.ridge is appropriate. Other possibilities are mars for multivariate adaptive regression splines and bruto for adaptive backfitting of additive splines. Use the . operator to quote specified functions.
trace: logical indicating whether iteration information is printed.
...: additional arguments to mda.start and method.

Details

Response types:: factor
Automatic tuning of grid parameter:: subclasses

The predict function for this model additionally accepts the following argument.

prior: prior class membership probabilities for prediction data if different from the training set.

Default values and further model details can be found in the source links below.

Examples

Run this code

# \donttest{
## Requires prior installation of suggested package mda to run

fit(Species ~ ., data = iris, model = MDAModel)
# }