R/predict.ridgeLinear.R

In ridge: Ridge Regression with Automatic Selection of the Penalty Parameter

## predict method for linear ridge regression models

#' @rdname predict
#' @export
#' @importFrom stats na.pass terms model.matrix model.frame delete.response .checkMFClasses coef
predict.ridgeLinear <- function(object, newdata,  
    na.action = na.pass, all.coef = FALSE, ...)
  {
    tt <- terms(object)
    
    if (!inherits(object, "ridgeLinear")) {
      warning("calling predict.ridgeLinear(<fake-ridgeLinear-object>) ...")
    }
    
    if (missing(newdata) || is.null(newdata)) {
      newdata <- object$model_frame
    }
    
  
      Terms <- delete.response(tt)
      m <- model.frame(Terms, newdata, na.action = na.action, 
                       xlev = object$xlevels)
      if (!is.null(cl <- attr(Terms, "dataClasses"))) 
        .checkMFClasses(cl, m)
      mm <- X <- model.matrix(Terms, m, contrasts.arg = object$contrasts)
      offset <- rep(0, nrow(X))
      if (!is.null(off.num <- attr(tt, "offset"))) 
        for (i in off.num) offset <- offset + eval(attr(tt, 
                                                        "variables")[[i + 1]], newdata)
      if (!is.null(object$call$offset)) 
        offset <- offset + eval(object$call$offset, newdata)
    
    beta <- coef(object, all.coef = all.coef)
    
    if(all.coef)
      res <- apply(beta, 1, function(x){drop(as.matrix(mm) %*% x)})
    else
      res <- drop(as.matrix(mm) %*% beta)
    res
  }