61 lines
1.9 KiB
R
61 lines
1.9 KiB
R
#' Gets estimated SDR basis.
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#'
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#' Returns the SDR basis matrix for dimension \code{k}, i.e. returns the
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#' cve-estimate with dimension \eqn{p\times k}{p x k}.
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#'
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#' @param object instance of \code{cve} as output from \code{\link{cve}} or
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#' \code{\link{cve.call}}.
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#' @param k the SDR dimension.
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#' @param ... ignored.
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#'
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#' @return dir the matrix of CS or CMS of given dimension
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#'
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#' @examples
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#' # set dimensions for simulation model
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#' p <- 8 # sample dimension
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#' k <- 2 # real dimension of SDR subspace
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#' n <- 200 # samplesize
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#' # create B for simulation
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#' b1 <- rep(1 / sqrt(p), p)
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#' b2 <- (-1)^seq(1, p) / sqrt(p)
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#' B <- cbind(b1, b2)
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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(n * p), n, p)
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#' # simulate response variable
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#' # y = f(B'x) + err
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#' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2)
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#' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(100)
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#' # calculate cve for k = 1, ..., 5
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#' cve.obj <- cve(y ~ x, max.dim = 5)
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#' # get cve-estimate for B with dimensions (p, k = 2)
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#' B2 <- coef(cve.obj, k = 2)
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#'
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#' # Projection matrix on span(B)
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#' # equivalent to `B %*% t(B)` since B is semi-orthonormal
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#' PB <- B %*% solve(t(B) %*% B) %*% t(B)
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#' # Projection matrix on span(B2)
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#' # equivalent to `B2 %*% t(B2)` since B2 is semi-orthonormal
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#' PB2 <- B2 %*% solve(t(B2) %*% B2) %*% t(B2)
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#' # compare estimation accuracy by Frobenius norm of difference of projections
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#' norm(PB - PB2, type = 'F')
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#'
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#' @method coef cve
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#' @aliases coef.cve
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#' @rdname coef.cve
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#' @export
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coef.cve <- function(object, k, ...) {
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if (missing(k)) {
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Bs <- list()
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for (k in names(object$res)) {
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Bs[[k]] <- object$res[[k]]$B
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}
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return(Bs)
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} else if (k %in% names(object$res)) {
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return(object$res[[as.character(k)]]$B)
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} else {
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stop("Requested dimension `k` not computed.")
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}
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}
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