Tune the regularization parameter lambda for a sup-norm classifier by cross-validation.
cv.supclass(
x,
y,
model = c("logistic", "psvm", "svm"),
penalty = c("lasso", "scad"),
start = NULL,
control = list(),
nfolds = 5L,
stratified = TRUE,
...
)An S3 object of class cv.supclass.
A numeric matrix representing the design matrix. No missing valus
are allowed. The coefficient estimates for constant columns will be
zero. Thus, one should set the argument intercept to TRUE
to include an intercept term instead of adding an all-one column to
x.
An integer vector, a character vector, or a factor vector representing the response label.
A charactor vector specifying the classification model. The
available options are "logistic" for multi-nomial logistic
regression model, "psvm" for proximal support vector machine
(PSVM), "svm" for multi-category support vector machine.
A charactor vector specifying the penalty function for the
sup-norms. The available options are "lasso" for sup-norm
regularization proposed by Zhang et al. (2008) and "scad" for
supSCAD regularization proposed by Li & Zhang (2021).
A numeric matrix representing the starting values for the quadratic approximation procedure behind the scene.
A list with named elements.
A positive integer specifying the number of folds for
cross-validation. Five-folds cross-validation will be used by default.
An error will be thrown out if the nfolds is specified to be less
than 2.
A logical value indicating if the cross-validation
procedure should be stratified by the response label. The default value
is TRUE to ensure the same number of categories be used in
validation and training.
Other arguments passed to supclass.