R/methods.r

In SCnext/SCGLR: Supervised Component Generalized Linear Regression

#'@title Regularization criterion types
#'@export
#'@param phi character string describing structural relevance used in the regularization process.
#' Allowed values are "vpi" for Variable Powered Inertia and "cv" for Component Variance. Default to "vpi".
#'@param l is an integer argument (>1) tuning the importance of variable bundle locality.
#'@param s is a numeric argument (in [0,1]) tuning the strength of structural relevance with respect to goodness of fit.
#'@param maxiter integer for maximum number of iterations of \code{SR} function
#'@param epsilon positive convergence threshold
#'@param bailout integer argument
methodSR <- function(phi="vpi", l=1, s=1/2, maxiter=1000, epsilon=1e-6, bailout=10) {
  
  # check arguments
  if(!(phi %in% c("vpi","cv")))
    stop("phi should be \"vpi\" or \"cv\"")
  if(!is.numeric(l) || l<1)
    stop("l must be greater than 1")
  if(!is.numeric(s) || s<0 || s>1)
    stop("s must be between 0 and 1")
  if(!is.numeric(maxiter) || maxiter<1)
    stop("maxiter must be an integer greater than 1")
  if(!is.numeric(epsilon) || epsilon<=0)
    stop("epsilon must be a positive numeric")
  if(!is.numeric(bailout) || bailout<1)
    stop("bailout must be an integer greater than 1")
  
  structure(
    list(
      method = "sr",
      phi = phi,
      l = l,
      s = s,
      maxiter = maxiter,
      epsilon = epsilon,
      bailout = bailout
    ),
    class = "method.SCGLR",
    description = "Method iterative normed gradient (ING) for Structural Relevance"
  )
}