R/hsvmpath.R

In gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

hsvmpath <- function(x, y, nlam, flmin, ulam, isd, intr, eps, dfmax, pmax, jd,
                     pf, pf2, maxit, lam2, delta, nobs, nvars, vnames) {
################################################################################
  ## data setup
  y <- as.factor(y)
  y <- c(-1, 1)[as.numeric(y)]
  if (!all(y %in% c(-1, 1)))
    stop("y should be a factor with two levels")
  if (delta < 0)
    stop("delta must be non-negative")
  delta <- as.double(delta)
  storage.mode(x) <- "double"
################################################################################
  ## call Fortran core
  fit <- .Fortran("hsvmlassoNET", delta, lam2, nobs, nvars, x, as.double(y), jd,
                  pf, pf2, dfmax, pmax, nlam, flmin, ulam, eps, isd, intr,
                  maxit, nalam = integer(1), b0 = double(nlam),
                  beta = double(pmax * nlam), ibeta = integer(pmax),
                  nbeta = integer(nlam), alam = double(nlam),
                  npass = integer(1), jerr = integer(1), PACKAGE = "gcdnet")
################################################################################
  ## output
  outlist <- getoutput(fit, maxit, pmax, nvars, vnames)
  outlist <- c(outlist, list(npasses = fit$npass, jerr = fit$jerr))
  class(outlist) <- c("hsvmpath")
  outlist
}