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