R/methods.R

In julien-gibaud/FactorsSCGLR: Supervised Component-based Generalized Linear Regression with Factors

#'@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 (between 0 and 1) tuning the strength of structural relevance with respect to goodness of fit.
#'@param maxiter integer for maximum number of iterations of 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#1000
  ),
  class="method.SCGLR",
  description="Method iterative normed gradient (ING) for Structural Relevance"
  )
}