48 lines
1.3 KiB
R
48 lines
1.3 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/directions.R
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\name{directions.cve}
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\alias{directions.cve}
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\alias{directions}
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\title{Computes projected training data \code{X} for given dimension `k`.}
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\usage{
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\method{directions}{cve}(object, k, ...)
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}
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\arguments{
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\item{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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\item{k}{SDR dimension to use for projection.}
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\item{...}{ignored (no additional arguments).}
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}
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\value{
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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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\description{
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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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\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{
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\code{\link{cve}}
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}
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