R/erpath.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

erpath <- function(x, y, nlam, flmin, ulam, isd, intr, eps, dfmax, pmax, jd, pf,
                   pf2, maxit, lam2, omega, nobs, nvars, vnames) {
###############################################################################
  ## data setup
  storage.mode(y) <- "double"
  storage.mode(x) <- "double"
  if (omega <= 0 || omega >= 1)
    stop("omega must be in (0,1)")
  omega <- as.double(omega)
###############################################################################
  ## call Fortran core
  fit <- .Fortran("erlassoNET", omega, lam2, nobs, nvars, x, 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("erpath")
  outlist
}