R/predict.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

##' @export
predict.hsvmpath <- function(object, newx, s = NULL, type = c("class", "link"),
                             ...) {
  type <- match.arg(type)
  b0 <- t(as.matrix(object$b0))
  rownames(b0) <- "(Intercept)"
  nbeta <- rbind2(b0, object$beta)
  if (!is.null(s)) {
    vnames <- dimnames(nbeta)[[1]]
    dimnames(nbeta) <- list(NULL, NULL)
    lambda <- object$lambda
    lamlist <- lambda.interp(lambda, s)
    nbeta <- nbeta[, lamlist$left, drop = FALSE] %*%
      Diagonal(x = lamlist$frac) +
      nbeta[, lamlist$right, drop = FALSE] %*%
      Diagonal(x=1-lamlist$frac)
    dimnames(nbeta) <- list(vnames, paste(seq(along = s)))
  }
  nfit <- as.matrix(as.matrix(cbind2(1, newx)) %*% nbeta)
  switch(type, link = nfit, class = ifelse(nfit > 0, 1, -1))
}