R/cv.lspath.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

##' @export
cv.lspath <- function(outlist, lambda, x, y, foldid, pred.loss, delta, omega) {
  typenames <- c(misclass = "Misclassification Error",
                 loss = "Least Square Loss")
  if (pred.loss == "default")
    pred.loss <- "loss"
  if (!match(pred.loss, c("loss"), FALSE)) {
    warning("Only 'loss' available for least squares regression; 'loss' used")
    pred.loss <- "loss"
  }
  ## Turn y into c(0,1)
  y <- as.double(y)
  nfolds <- max(foldid)
  predmat <- matrix(NA, length(y), length(lambda))
  nlams <- double(nfolds)
  for (i in seq(nfolds)) {
    which <- foldid == i
    fitobj <- outlist[[i]]
    preds <- predict(fitobj, x[which, , drop = FALSE], type = "link")
    nlami <- length(outlist[[i]]$lambda)
    predmat[which, seq(nlami)] <- preds
    nlams[i] <- nlami
  }
  cvraw <- (y-predmat)^2
  N <- length(y) - apply(is.na(predmat), 2, sum)
  cvm <- apply(cvraw, 2, mean, na.rm = TRUE)
  cvsd <- sqrt(apply(scale(cvraw, cvm, FALSE)^2,
                     2, mean, na.rm = TRUE)/(N - 1))
  list(cvm = cvm, cvsd = cvsd, name = typenames[pred.loss])
}