71 lines
2.0 KiB
R
71 lines
2.0 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/CVE.R
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\name{cve}
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\alias{cve}
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\alias{cve.call}
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\title{Implementation of the CVE method.}
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\usage{
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cve(formula, data, method = "simple", ...)
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cve.call(X, Y, method = "simple", nObs = nrow(X)^0.5, k, ...)
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}
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\arguments{
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\item{formula}{Formel for the regression model defining `X`, `Y`.
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See: \code{\link{formula}}.}
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\item{data}{data.frame holding data for formula.}
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\item{method}{The different only differe in the used optimization.
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All of them are Gradient based optimization on a Stiefel manifold.
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\itemize{
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\item "simple" Simple reduction of stepsize.
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\item "sgd" stocastic gradient decent.
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\item TODO: further
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}}
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\item{...}{Further parameters depending on the used method.}
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\item{X}{Data}
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\item{Y}{Responces}
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\item{nObs}{as describet in the Paper.}
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\item{k}{guess for SDR dimension.}
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\item{nObs}{Like in the paper.}
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\item{...}{Method specific parameters.}
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}
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\description{
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Conditional Variance Estimator (CVE) is a novel sufficient dimension
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reduction (SDR) method assuming a model
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\deqn{Y \sim g(B'X) + \epsilon}{Y ~ g(B'X) + epsilon}
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where B'X is a lower dimensional projection of the predictors.
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}
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\examples{
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library(CVE)
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# sample dataset
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ds <- dataset("M5")
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# call ´cve´ with default method (aka "simple")
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dr.simple <- cve(ds$Y ~ ds$X, k = ncol(ds$B))
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# plot optimization history (loss via iteration)
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plot(dr.simple, main = "CVE M5 simple")
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# call ´cve´ with method "linesearch" using ´data.frame´ as data.
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data <- data.frame(Y = ds$Y, X = ds$X)
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# Note: ´Y, X´ are NOT defined, they are extracted from ´data´.
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dr.linesearch <- cve(Y ~ ., data, method = "linesearch", k = ncol(ds$B))
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plot(dr.linesearch, main = "CVE M5 linesearch")
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}
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\references{
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Fertl L., Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
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}
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\seealso{
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\code{\link{formula}}. For a complete parameters list (dependent on
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the method) see \code{\link{cve_simple}}, \code{\link{cve_sgd}}
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}
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