Flexible and Penalized Discriminant Analysis Models
Performs flexible discriminant analysis.
FDAModel( theta = NULL, dimension = NULL, eps = .Machine$double.eps, method = .(mda::polyreg), ... ) PDAModel(lambda = 1, df = NULL, ...)
theta |
optional matrix of class scores, typically with number of columns less than one minus the number of classes. |
dimension |
dimension of the discriminant subspace, less than the number of classes, to use for prediction. |
eps |
numeric threshold for small singular values for excluding discriminant variables. |
method |
regression function used in optimal scaling. The default of
linear regression is provided by |
... |
additional arguments to |
lambda |
shrinkage penalty coefficient. |
df |
alternative specification of |
factor
FDAModel: nprune, degree*
PDAModel: lambda
* included only in randomly sampled grid points
The predict function for this model additionally accepts the
following argument.
priorprior 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.
MLModel class object.
## Requires prior installation of suggested package mda to run fit(Species ~ ., data = iris, model = FDAModel) ## Requires prior installation of suggested package mda to run fit(Species ~ ., data = iris, model = PDAModel)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.