47 lines
1.3 KiB
R
47 lines
1.3 KiB
R
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/CVE.R
|
|
\name{cve}
|
|
\alias{cve}
|
|
\alias{cve.call}
|
|
\title{Implementation of the CVE method.}
|
|
\usage{
|
|
cve(formula, data, method = "simple", ...)
|
|
|
|
cve.call(X, Y, method = "simple", nObs = nrow(X)^0.5, k, ...)
|
|
}
|
|
\arguments{
|
|
\item{formula}{Formel for the regression model defining `X`, `Y`.
|
|
See: \code{\link{formula}}.}
|
|
|
|
\item{data}{data.frame holding data for formula.}
|
|
|
|
\item{method}{The different only differe in the used optimization.
|
|
All of them are Gradient based optimization on a Stiefel manifold.
|
|
\itemize{
|
|
\item "simple" Simple reduction of stepsize.
|
|
\item "linesearch" determines stepsize with backtracking linesearch
|
|
using Armijo-Wolf conditions.
|
|
\item TODO: further
|
|
}}
|
|
|
|
\item{...}{Further parameters depending on the used method.
|
|
TODO: See ...}
|
|
}
|
|
\description{
|
|
Conditional Variance Estimator (CVE) is a novel sufficient dimension
|
|
reduction (SDR) method assuming a model
|
|
\deqn{Y \sim g(B'X) + \epsilon}{Y ~ g(B'X) + epsilon}
|
|
where B'X is a lower dimensional projection of the predictors.
|
|
}
|
|
\examples{
|
|
library(CVE)
|
|
ds <- dataset("M5")
|
|
X <- ds$X
|
|
Y <- ds$Y
|
|
dr <- cve(Y ~ X, k = 1)
|
|
|
|
}
|
|
\references{
|
|
Fertl L, Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
|
|
}
|