CVE/CVarE/man/directions.cve.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/directions.R
\name{directions.cve}
\alias{directions.cve}
\alias{directions}
\title{Computes projected training data \code{X} for given dimension `k`.}
\usage{
\method{directions}{cve}(object, k, ...)
}
\arguments{
\item{object}{an object of class \code{"cve"}, usually, a result of a call to
\code{\link{cve}} or \code{\link{cve.call}}.}

\item{k}{SDR dimension to use for projection.}

\item{...}{ignored (no additional arguments).}
}
\value{
the \eqn{n\times k}{n x k} dimensional matrix \eqn{X B} where \eqn{B}
 is the  cve-estimate for dimension \eqn{k}.
}
\description{
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}.
}
\examples{
# create B for simulation (k = 1)
B <- rep(1, 5) / sqrt(5)
set.seed(21)
# creat predictor data x ~ N(0, I_p)
x <- matrix(rnorm(500), 100, 5)
# simulate response variable
#    y = f(B'x) + err
# with f(x1) = x1 and err ~ N(0, 0.25^2)
y <- x \%*\% B + 0.25 * rnorm(100)
# calculate cve with method 'mean' for k = 1
set.seed(21)
cve.obj.mean <- cve(y ~ x, k = 1, method = 'mean')
# get projected data for k = 1
x.proj <- directions(cve.obj.mean, k = 1)
# plot y against projected data
plot(x.proj, y)

}
\seealso{
\code{\link{cve}}
}