48 lines
1.5 KiB
R
48 lines
1.5 KiB
R
#' @export
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directions <- function(object, k, ...) {
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UseMethod("directions")
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}
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#' Computes projected training data \code{X} for given dimension `k`.
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#'
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#' Returns \eqn{B'X}. That is, it computes the projection of the \eqn{n x p}
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#' design matrix \eqn{X} on the column space of \eqn{B} of dimension \eqn{k}.
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#'
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#' @param object an object of class \code{"cve"}, usually, a result of a call to
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#' \code{\link{cve}} or \code{\link{cve.call}}.
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#' @param k SDR dimension to use for projection.
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#' @param ... ignored (no additional arguments).
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#'
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#' @return the \eqn{n\times k}{n x k} dimensional matrix \eqn{X B} where \eqn{B}
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#' is the cve-estimate for dimension \eqn{k}.
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#'
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#' @examples
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#' # create B for simulation (k = 1)
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#' B <- rep(1, 5) / sqrt(5)
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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, 5)
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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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#' # calculate cve with method 'mean' for k = 1
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#' set.seed(21)
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#' cve.obj.mean <- cve(y ~ x, k = 1, method = 'mean')
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#' # get projected data for k = 1
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#' x.proj <- directions(cve.obj.mean, k = 1)
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#' # plot y against projected data
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#' plot(x.proj, y)
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#'
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#' @seealso \code{\link{cve}}
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#'
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#' @method directions cve
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#' @aliases directions directions.cve
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#' @export
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directions.cve <- function(object, k, ...) {
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if (!(k %in% names(object$res))) {
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stop("SDR directions for requested dimension `k` not computed.")
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}
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return(object$X %*% object$res[[as.character(k)]]$B)
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}
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