63 lines
1.6 KiB
R
63 lines
1.6 KiB
R
#' Predict method for CVE Fits.
|
|
#'
|
|
#' Predict responces using reduced data with \code{\link{mars}}.
|
|
#'
|
|
#' @param object instance of class \code{cve} (result of \code{cve},
|
|
#' \code{cve.call}).
|
|
#' @param newdata Matrix of the new data to be predicted.
|
|
#' @param dim dimension of SDR space to be used for data projecition.
|
|
#' @param ... further arguments passed to \code{\link{mars}}.
|
|
#'
|
|
#' @return prediced response of data \code{newdata}.
|
|
#'
|
|
#' @examples
|
|
#' # create B for simulation
|
|
#' B <- rep(1, 5) / sqrt(5)
|
|
#'
|
|
#' set.seed(21)
|
|
#' # creat predictor data x ~ N(0, I_p)
|
|
#' x <- matrix(rnorm(500), 100)
|
|
#'
|
|
#' # simulate response variable
|
|
#' # y = f(B'x) + err
|
|
#' # with f(x1) = x1 and err ~ N(0, 0.25^2)
|
|
#' y <- x %*% B + 0.25 * rnorm(100)
|
|
#'
|
|
#' x.train <- x[1:80, ]
|
|
#' x.test <- x[81:100, ]
|
|
#' y.train <- y[1:80, ]
|
|
#' y.test <- y[81:100, ]
|
|
#'
|
|
#' # calculate cve with method 'simple' for k = 1
|
|
#' cve.obj.simple <- cve(y.train ~ x.train, k = 1)
|
|
#'
|
|
#' # predict y.test from x.test
|
|
#' yhat <- predict(cve.obj.simple, x.test, 1)
|
|
#'
|
|
#' # plot prediction against y.test
|
|
#' plot(yhat, y.test)
|
|
#' @seealso \code{\link{cve}}, \code{\link{cve.call}} or \pkg{\link{mars}}.
|
|
#'
|
|
#' @rdname predict.cve
|
|
#'
|
|
#' @importFrom mda mars
|
|
#' @method predict cve
|
|
#' @export
|
|
predict.cve <- function(object, newdata, dim, ...) {
|
|
if (missing(newdata)) {
|
|
stop("No data supplied.")
|
|
}
|
|
if (missing(dim)) {
|
|
stop("No dimension supplied.")
|
|
}
|
|
|
|
if (!is.matrix(newdata)) {
|
|
newdata <- matrix(newdata, nrow = 1L)
|
|
}
|
|
|
|
B <- object$res[[as.character(dim)]]$B
|
|
|
|
model <- mda::mars(object$X %*% B, object$Y)
|
|
predict(model, newdata %*% B)
|
|
}
|