101 lines
2.9 KiB
R
101 lines
2.9 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{Conditional Variance Estimator (CVE).}
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\usage{
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cve(formula, data, method = "simple", max.dim = 10L, ...)
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cve.call(X, Y, method = "simple", nObs = sqrt(nrow(X)), h = NULL,
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min.dim = 1L, max.dim = 10L, k = NULL, momentum = 0, tau = 1,
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tol = 0.001, slack = 0, gamma = 0.5, V.init = NULL,
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epochs = 50L, attempts = 10L, logger = NULL)
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}
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\arguments{
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\item{formula}{an object of class \code{"formula"} which is a symbolic
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description of the model to be fitted.}
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\item{data}{an optional data frame, containing the data for the formula if
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supplied.}
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\item{method}{specifies the CVE method variation as one of
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\itemize{
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\item "simple" exact implementation as described in the paper listed
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below.
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\item "weighted" variation with addaptive weighting of slices.
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}}
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\item{max.dim}{upper bounds for \code{k}, (ignored if \code{k} is supplied).}
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\item{...}{Parameters passed on to \code{cve.call}.}
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\item{X}{data matrix with samples in its rows.}
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\item{Y}{Responses (1 dimensional).}
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\item{nObs}{parameter for choosing bandwidth \code{h} using
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\code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied).}
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\item{min.dim}{lower bounds for \code{k}, (ignored if \code{k} is supplied).}
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\item{k}{Dimension of lower dimensional projection, if \code{k} is given
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only the specified dimension \code{B} matrix is estimated.}
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\item{tau}{Initial step-size.}
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\item{tol}{Tolerance for break condition.}
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\item{epochs}{maximum number of optimization steps.}
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\item{attempts}{number of arbitrary different starting points.}
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\item{logger}{a logger function (only for advanced user, significantly slows
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down the computation).}
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}
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\value{
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dr is a S3 object of class \code{cve} with named properties:
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\itemize{
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\item X: Original training data,
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\item Y: Responce of original training data,
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\item method: Name of used method,
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\item call: The method call
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}
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as well as indexed entries \code{dr[[k]]} storing the k-dimensional SDR
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projection matrices.
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dr is a list which contains:
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\itemize{
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\item dir: dir[[d]] is the central space with d-dimension
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d = 1, 2, ..., p reduced direction of different dimensions
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\item y: the value of response
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\item idx: the index of variables which survives after screening
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\item max.dim: the largest dimensions of CS or CMS which have been calculated in mave function
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\item ky: parameter used for DIM for selection
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\item x: the original training data
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}
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}
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\description{
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TODO: reuse of package description and details!!!!
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}
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\examples{
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library(CVE)
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# create dataset
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n <- 200
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p <- 12
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X <- matrix(rnorm(n * p), n, p)
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B <- cbind(c(1, rep(0, p - 1)), c(0, 1, rep(0, p - 2)))
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Y <- X \%*\% B
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Y <- Y[, 1L]^2 + Y[, 2L]^2 + rnorm(n, 0, 0.3)
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# Call the CVE method.
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dr <- cve(Y ~ X)
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(B <- basis(dr, 2))
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}
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\seealso{
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For a detailed description of \code{formula} see
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[\code{\link{formula}}].
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}
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