cv.abclass: Tune Angle-Based Classifiers by Cross-Validation

Description

Tune the regularization parameter for an angle-based large-margin classifier by cross-validation.

Usage

cv.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nfolds = 5L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)

Value

An S3 object of class cv.abclass.

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 intercept to TRUE to include an intercept term instead of adding an all-one column to x.
y: An integer vector, a character vector, or a factor vector representing the response label.
intercept: A logical value indicating if an intercept should be considered in the model. The default value is TRUE and the intercept is excluded from regularization.
weight: A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
loss: A character value specifying the loss function. The available options are "logistic" for the logistic deviance loss, "boost" for the exponential loss approximating Boosting machines, "hinge-boost" for hybrid of SVM and AdaBoost machine, and "lum" for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
control: A list of control parameters. See abclass.control() for details.
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 nfolds is specified to be less than 2.
stratified: 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.
alignment: A character vector specifying how to align the lambda sequence used in the main fit with the cross-validation fits. The available options are "fraction" for allowing cross-validation fits to have their own lambda sequences and "lambda" for using the same lambda sequence of the main fit. The option "lambda" will be applied if a meaningful lambda is specified. The default value is "fraction".
refit: A logical value or a named list specifying if and how a refit for those selected predictors should be performed. The default valie is FALSE.
...: Other control parameters passed to abclass.control().