cv.supclass {abclass} | R Documentation |
Tune Sup-Norm Classifiers by Cross-Validation
Description
Tune the regularization parameter lambda for a sup-norm classifier by cross-validation.
Usage
cv.supclass(
x,
y,
model = c("logistic", "psvm", "svm"),
penalty = c("lasso", "scad"),
start = NULL,
control = list(),
nfolds = 5L,
stratified = TRUE,
...
)
Arguments
x |
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 |
y |
An integer vector, a character vector, or a factor vector representing the response label. |
model |
A charactor vector specifying the classification model. The
available options are |
penalty |
A charactor vector specifying the penalty function for the
sup-norms. The available options are |
start |
A numeric matrix representing the starting values for the quadratic approximation procedure behind the scene. |
control |
A list with named elements. |
nfolds |
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 |
stratified |
A logical value indicating if the cross-validation
procedure should be stratified by the response label. The default value
is |
... |
Other arguments passed to |
Value
An S3 object of class cv.supclass
.