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fix: some smaller change in the docs

This commit is contained in:
Daniel Kapla 2020-02-26 13:44:53 +01:00
parent 377b3503ab
commit 4696620363
7 changed files with 29 additions and 28 deletions

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@ -5,8 +5,8 @@ directions <- function(object, k, ...) {
#' Computes projected training data \code{X} for given dimension `k`.
#'
#' Returns \eqn{B'X}. That is the dimensional design matrix \eqn{X} on the
#' columnspace of the cve-estimate for given dimension \eqn{k}.
#' Returns \eqn{B'X}. That is, it computes the projection of the \eqn{n x p}
#' design matrix \eqn{X} on the column space of \eqn{B} of dimension \eqn{k}.
#'
#' @param object an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.

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@ -37,7 +37,6 @@
#' # calculate cve with method 'simple' for k = 1
#' set.seed(21)
#' cve.obj.simple <- cve(y ~ x, k = k)
#' print(cve.obj.simple$res$'1'$h)
#' print(estimate.bandwidth(x, k = k))
#' @export
estimate.bandwidth <- function (X, k, nObs, version = 1L) {

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@ -2,9 +2,9 @@
#'
#' Boxplots of the output \code{L} from \code{\link{cve}} over \code{k} from
#' \code{min.dim} to \code{max.dim}. For given \code{k}, \code{L} corresponds
#' to \eqn{L_n(V, X_i)} where \eqn{V} is a stiefel manifold element as
#' minimizer of
#' \eqn{L_n(V)}, for further details see Fertl, L. and Bura, E. (2019).
#' to \eqn{L_n(V, X_i)} where \eqn{V} is the minimizer of \eqn{L_n(V)} where
#' \eqn{V} is an element of a Stiefel manifold (see
#' Fertl, L. and Bura, E. (2019)).
#'
#' @param x an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.

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@ -1,7 +1,7 @@
#' Predict method for CVE Fits.
#'
#' Predict response using projected data \eqn{B'C} by fitting
#' \eqn{g(B'C) + \epsilon} using \code{\link{mars}}.
#' Predict response using projected data. The forward model \eqn{g(B' X)} is
#' estimated with \code{\link{mars}} in the \code{\pkg{mda}} package.
#'
#' @param object an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.
@ -9,7 +9,7 @@
#' @param k dimension of SDR space to be used for data projection.
#' @param ... further arguments passed to \code{\link{mars}}.
#'
#' @return prediced response at \code{newdata}.
#' @return prediced respone(s) for \code{newdata}.
#'
#' @examples
#' # create B for simulation

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@ -122,31 +122,32 @@ predict_dim_wilcoxon <- function(object, p.value = 0.05) {
))
}
#' Estimate Dimension of Reduction Space.
#' Estimate Dimension of the Sufficient Reduction.
#'
#' This function estimates the dimension of the mean dimension reduction space,
#' i.e. number of columns of \eqn{B} matrix. The default method \code{'CV'}
#' performs l.o.o cross-validation using \code{mars}. Given
#' \code{k = min.dim, ..., max.dim} a cross-validation via \code{mars} is
#' This function estimates the dimension, i.e. the rank of \eqn{B}. The default
#' method \code{'CV'} performs leave-one-out (LOO) cross-validation using
#' \code{mars} as follows for \code{k = min.dim, ..., max.dim} a
#' cross-validation via \code{mars} is
#' performed on the dataset \eqn{(Y_i, B_k' X_i)_{i = 1, ..., n}} where
#' \eqn{B_k} is the \eqn{p \times k}{p x k} dimensional CVE estimate. The
#' estimated SDR dimension is the \eqn{k} where the
#' cross-validation mean squared error is minimal. The method \code{'elbow'}
#' estimates the dimension via \eqn{k = argmin_k L_n(V_{p - k})} where
#' \eqn{V_{p - k}} is space that is orthogonal to the columns-space of the CVE estimate of \eqn{B_k}. Method \code{'wilcoxon'} is similar to \code{'elbow'}
#' but finds the minimum using the wilcoxon-test.
#' \eqn{V_{p - k}} is the space that is orthogonal to the column space of the
#' CVE estimate of \eqn{B_k}. Method \code{'wilcoxon'} finds the minimum using
#' the Wilcoxon test.
#'
#' @param object an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.
#' @param method This parameter specify which method will be used in dimension
#' estimation. It provides three methods \code{'CV'} (default), \code{'elbow'},
#' and \code{'wilcoxon'} to estimate the dimension of the SDR.
#' @param method This parameter specifies which method is used in dimension
#' estimation. It provides three options: \code{'CV'} (default),
#' \code{'elbow'} and \code{'wilcoxon'}.
#' @param ... ignored.
#'
#' @return list with
#' @return A \code{list} with
#' \describe{
#' \item{}{cretirion of method for \code{k = min.dim, ..., max.dim}.}
#' \item{k}{estimated dimension as argmin over \eqn{k} of criterion.}
#' \item{}{criterion for method and \code{k = min.dim, ..., max.dim}.}
#' \item{k}{estimated dimension is the minimizer of the criterion.}
#' }
#'
#' @examples

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@ -1,7 +1,6 @@
#' Prints a summary of a \code{cve} result.
#' Prints summary statistics of the \eqn{L} \code{cve} component.
#'
#' Prints a summary statistics of output \code{L} from \code{cve} for
#' \code{k = min.dim, ..., max.dim}.
#' Prints a summary statistics of the \code{L} component of a \code{cve} object #' for \code{k = min.dim, ..., max.dim}.
#'
#' @param object an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.

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@ -1,9 +1,11 @@
#' Draws a sample from the invariant measure on the Stiefel manifold
#' Random sample from Stiefel manifold.
#'
#' Draws a random sample from the invariant measure on the Stiefel manifold
#' \eqn{S(p, q)}.
#'
#' @param p row dimension
#' @param q col dimension
#' @return \eqn{p \times q}{p x q} semi-orthogonal matrix.
#' @return A \eqn{p \times q}{p x q} semi-orthogonal matrix.
#' @examples
#' V <- rStiefel(6, 4)
#' @export