MachineShop (version 2.0.0)

MDAModel: Mixture Discriminant Analysis Model

Description

Performs mixture discriminant analysis.

Usage

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

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.

Value

MLModel class object.

Details

Response Types:

factor

Automatic Tuning of Grid Parameters:

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 for the NULL arguments and further model details can be found in the source links below.

See Also

mda, predict.mda, fit, resample

Examples

# NOT RUN {
fit(Species ~ ., data = iris, model = MDAModel)

# }