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*.pdf filter=lfs diff=lfs merge=lfs -text

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simulations/results/*
literature/*
CVE/src/*.o
CVE/src/*.so
CVE/src/*.dll
*.tgz
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*.tar.gz
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*.so
*.Rcheck/*
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CVE/DESCRIPTION
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Package: CVE Package: CVE
Type: Package Type: Package
Title: Conditional Variance Estimator for Sufficient Dimension Reduction Title: Conditional Variance Estimator for Sufficient Dimension Reduction
Version: 0.2 Version: 0.3
Date: 2019-12-20 Date: 2021-03-04
Author: Daniel Kapla <daniel@kapla.at>, Lukas Fertl <lukas.fertl@chello.at> Author: Daniel Kapla <daniel@kapla.at>, Lukas Fertl <lukas.fertl@chello.at>
Maintainer: Daniel Kapla <daniel@kapla.at> Maintainer: Daniel Kapla <daniel@kapla.at>
Description: Implementation of the Conditional Variance Estimation (CVE) method. Description: Implementation of the Conditional Variance Estimation (CVE) method.
License: GPL-3 License: GPL-3
Encoding: UTF-8 Encoding: UTF-8
Imports: stats,graphics,mda Imports: stats,mda
RoxygenNote: 6.1.1 RoxygenNote: 7.0.2

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CVE/NAMESPACE
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@ -2,7 +2,6 @@
S3method(coef,cve) S3method(coef,cve)
S3method(directions,cve) S3method(directions,cve)
S3method(plot,cve)
S3method(predict,cve) S3method(predict,cve)
S3method(summary,cve) S3method(summary,cve)
export(cve) export(cve)
@ -14,10 +13,6 @@ export(null)
export(predict_dim) export(predict_dim)
export(rStiefel) export(rStiefel)
import(stats) import(stats)
importFrom(graphics,boxplot)
importFrom(graphics,lines)
importFrom(graphics,plot)
importFrom(graphics,points)
importFrom(mda,mars) importFrom(mda,mars)
importFrom(stats,model.frame) importFrom(stats,model.frame)
importFrom(stats,rbinom) importFrom(stats,rbinom)

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CVE/R/CVE.R
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@ -3,7 +3,7 @@
#' Conditional Variance Estimation (CVE) is a novel sufficient dimension #' Conditional Variance Estimation (CVE) is a novel sufficient dimension
#' reduction (SDR) method for regressions satisfying \eqn{E(Y|X) = E(Y|B'X)}, #' reduction (SDR) method for regressions satisfying \eqn{E(Y|X) = E(Y|B'X)},
#' where \eqn{B'X} is a lower dimensional projection of the predictors and #' where \eqn{B'X} is a lower dimensional projection of the predictors and
#' \eqn{Y} is a univariate responce. CVE, #' \eqn{Y} is a univariate response. CVE,
#' similarly to its main competitor, the mean average variance estimation #' similarly to its main competitor, the mean average variance estimation
#' (MAVE), is not based on inverse regression, and does not require the #' (MAVE), is not based on inverse regression, and does not require the
#' restrictive linearity and constant variance conditions of moment based SDR #' restrictive linearity and constant variance conditions of moment based SDR
@ -11,7 +11,9 @@
#' continuous predictors and link function. Let \eqn{X} be a real #' continuous predictors and link function. Let \eqn{X} be a real
#' \eqn{p}-dimensional covariate vector. We assume that the dependence of #' \eqn{p}-dimensional covariate vector. We assume that the dependence of
#' \eqn{Y} and \eqn{X} is modelled by #' \eqn{Y} and \eqn{X} is modelled by
#'
#' \deqn{Y = g(B'X) + \epsilon} #' \deqn{Y = g(B'X) + \epsilon}
#'
#' where \eqn{X} is independent of \eqn{\epsilon} with positive definite #' where \eqn{X} is independent of \eqn{\epsilon} with positive definite
#' variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean #' variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
#' zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g} #' zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
@ -20,9 +22,28 @@
#' a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}. #' a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
#' Without loss of generality \eqn{B} is assumed to be orthonormal. #' Without loss of generality \eqn{B} is assumed to be orthonormal.
#' #'
#' Further, the extended Ensemble Conditional Variance Estimation (ECVE) is
#' implemented which is a SDR method in regressions with continuous response and
#' predictors. ECVE applies to general non-additive error regression models.
#'
#' \deqn{Y = g(B'X, \epsilon)}
#'
#' It operates under the assumption that the predictors can be replaced by a
#' lower dimensional projection without loss of information.It is a
#' semiparametric forward regression model based exhaustive sufficient dimension
#' reduction estimation method that is shown to be consistent under mild
#' assumptions.
#'
#' @author Daniel Kapla, Lukas Fertl, Bura Efstathia #' @author Daniel Kapla, Lukas Fertl, Bura Efstathia
#' @references Fertl, L. and Bura, E. (2019), Conditional Variance #'
#' Estimation for Sufficient Dimension Reduction. Working Paper. #' @references
#' [1] Fertl, L. and Bura, E. (2021), Conditional Variance
#' Estimation for Sufficient Dimension Reduction.
#' arXiv:2102.08782
#'
#' [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
#' Estimation for Sufficient Dimension Reduction.
#' arXiv:2102.13435
#' #'
#' @docType package #' @docType package
#' @useDynLib CVE, .registration = TRUE #' @useDynLib CVE, .registration = TRUE
@ -30,11 +51,52 @@
#' Conditional Variance Estimator (CVE). #' Conditional Variance Estimator (CVE).
#' #'
#' @description
#' This is the main function in the \code{CVE} package. It creates objects of #' This is the main function in the \code{CVE} package. It creates objects of
#' class \code{"cve"} to estimate the mean subspace. Helper functions that #' class \code{"cve"} to estimate the mean subspace. Helper functions that
#' require a \code{"cve"} object can then be applied to the output from this #' require a \code{"cve"} object can then be applied to the output from this
#' function. #' function.
#' #'
#' Conditional Variance Estimation (CVE) is a sufficient dimension reduction
#' (SDR) method for regressions studying \eqn{E(Y|X)}, the conditional
#' expectation of a response \eqn{Y} given a set of predictors \eqn{X}. This
#' function provides methods for estimating the dimension and the subspace
#' spanned by the columns of a \eqn{p\times k}{p x k} matrix \eqn{B} of minimal
#' rank \eqn{k} such that
#'
#' \deqn{E(Y|X) = E(Y|B'X)}
#'
#' or, equivalently,
#'
#' \deqn{Y = g(B'X) + \epsilon}
#'
#' where \eqn{X} is independent of \eqn{\epsilon} with positive definite
#' variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
#' zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
#' is an unknown, continuous non-constant function, and \eqn{B = (b_1,..., b_k)}
#' is a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
#'
#' Both the dimension \eqn{k} and the subspace \eqn{span(B)} are unknown. The
#' CVE method makes very few assumptions.
#'
#' A kernel matrix \eqn{\hat{B}}{Bhat} is estimated such that the column space
#' of \eqn{\hat{B}}{Bhat} should be close to the mean subspace \eqn{span(B)}.
#' The primary output from this method is a set of orthonormal vectors,
#' \eqn{\hat{B}}{Bhat}, whose span estimates \eqn{span(B)}.
#'
#' The method central implements the Ensemble Conditional Variance Estimation
#' (ECVE) as described in [2]. It augments the CVE method by applying an
#' ensemble of functions (parameter \code{func_list}) to the response to
#' estimate the central subspace. This corresponds to the generalization
#'
#' \deqn{F(Y|X) = F(Y|B'X)}
#'
#' or, equivalently,
#'
#' \deqn{Y = g(B'X, \epsilon)}
#'
#' where \eqn{F} is the conditional cumulative distribution function.
#'
#' @param formula an object of class \code{"formula"} which is a symbolic #' @param formula an object of class \code{"formula"} which is a symbolic
#' description of the model to be fitted like \eqn{Y\sim X}{Y ~ X} where #' description of the model to be fitted like \eqn{Y\sim X}{Y ~ X} where
#' \eqn{Y} is a \eqn{n}-dimensional vector of the response variable and #' \eqn{Y} is a \eqn{n}-dimensional vector of the response variable and
@ -46,41 +108,17 @@
#' @param method This character string specifies the method of fitting. The #' @param method This character string specifies the method of fitting. The
#' options are #' options are
#' \itemize{ #' \itemize{
#' \item "simple" implementation, #' \item \code{"mean"} method to estimate the mean subspace, see [1].
#' \item "weighted" variation with adaptive weighting of slices. #' \item \code{"central"} ensemble method to estimate the central subspace,
#' see [2].
#' \item \code{"weighted.mean"} variation of \code{"mean"} method with
#' adaptive weighting of slices, see [1].
#' \item \code{"weighted.central"} variation of \code{"central"} method with
#' adaptive weighting of slices, see [2].
#' } #' }
#' see Fertl, L. and Bura, E. (2019).
#' @param max.dim upper bounds for \code{k}, (ignored if \code{k} is supplied). #' @param max.dim upper bounds for \code{k}, (ignored if \code{k} is supplied).
#' @param ... optional parameters passed on to \code{cve.call}. #' @param ... optional parameters passed on to \code{\link{cve.call}}.
#' #'
#'
#' Conditional Variance Estimation (CVE) is a sufficient dimension reduction
#' (SDR) method for regressions studying \eqn{E(Y|X)}, the conditional
#' expectation of a response \eqn{Y} given a set of predictors \eqn{X}. This
#' function provides methods for estimating the dimension and the subspace
#' spanned by the columns of a \eqn{p\times k}{p x k} matrix \eqn{B} of minimal
#' rank \eqn{k} such that
#' \deqn{%
#' E(Y|X) = E(Y|B'X) %
#' }
#' or, equivalently,
#' \deqn{%
#' Y = g(B'X) + \epsilon %
#' }
#' where \eqn{X} is independent of \eqn{\epsilon} with positive definite
#' variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
#' zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
#' is an unknown, continuous non-constant function, and \eqn{B = (b_1,..., b_k)}
#' is a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
#'
#' Both the dimension \eqn{k} and the subspace \eqn{span(B)} are unknown. The
#' CVE method makes very few assumptions.
#'
#' A kernel matrix \eqn{\hat{B}}{Bhat} is estimated such that the column space
#' of \eqn{\hat{B}}{Bhat} should be close to the mean subspace \eqn{span(B)}.
#' The primary output from this method is a set of orthonormal vectors,
#' \eqn{\hat{B}}{Bhat}, whose span estimates \eqn{span(B)}.
#'
#' @return an S3 object of class \code{cve} with components: #' @return an S3 object of class \code{cve} with components:
#' \describe{ #' \describe{
#' \item{X}{design matrix of predictor vector used for calculating #' \item{X}{design matrix of predictor vector used for calculating
@ -108,40 +146,42 @@
#' #'
#' @examples #' @examples
#' # set dimensions for simulation model #' # set dimensions for simulation model
#' p <- 8 #' p <- 5
#' k <- 2 #' k <- 2
#' # create B for simulation #' # create B for simulation
#' b1 <- rep(1 / sqrt(p), p) #' b1 <- rep(1 / sqrt(p), p)
#' b2 <- (-1)^seq(1, p) / sqrt(p) #' b2 <- (-1)^seq(1, p) / sqrt(p)
#' B <- cbind(b1, b2) #' B <- cbind(b1, b2)
#' # sample size #' # sample size
#' n <- 200 #' n <- 100
#' set.seed(21) #' set.seed(21)
#'
#' # creat predictor data x ~ N(0, I_p) #' # creat predictor data x ~ N(0, I_p)
#' x <- matrix(rnorm(n * p), n, p) #' x <- matrix(rnorm(n * p), n, p)
#' # simulate response variable #' # simulate response variable
#' # y = f(B'x) + err #' # y = f(B'x) + err
#' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2) #' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2)
#' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(100) #' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(n)
#' # calculate cve with method 'mean' for k unknown in 1, ..., 4 #'
#' cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'mean' #' # calculate cve with method 'mean' for k unknown in 1, ..., 3
#' cve.obj.s <- cve(y ~ x, max.dim = 2) # default method 'mean'
#' # calculate cve with method 'weighed' for k = 2 #' # calculate cve with method 'weighed' for k = 2
#' cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean') #' cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean')
#' # estimate dimension from cve.obj.s #' B2 <- coef(cve.obj.s, k = 2)
#' khat <- predict_dim(cve.obj.s)$k #'
#' # get cve-estimate for B with dimensions (p, k = khat)
#' B2 <- coef(cve.obj.s, k = khat)
#' # get projected X data (same as cve.obj.s$X %*% B2) #' # get projected X data (same as cve.obj.s$X %*% B2)
#' proj.X <- directions(cve.obj.s, k = khat) #' proj.X <- directions(cve.obj.s, k = 2)
#' # plot y against projected data #' # plot y against projected data
#' plot(proj.X[, 1], y) #' plot(proj.X[, 1], y)
#' plot(proj.X[, 2], y) #' plot(proj.X[, 2], y)
#'
#' # creat 10 new x points and y according to model #' # creat 10 new x points and y according to model
#' x.new <- matrix(rnorm(10 * p), 10, p) #' x.new <- matrix(rnorm(10 * p), 10, p)
#' y.new <- (x.new %*% b1)^2 + 2 * (x.new %*% b2) + 0.25 * rnorm(10) #' y.new <- (x.new %*% b1)^2 + 2 * (x.new %*% b2) + 0.25 * rnorm(10)
#' # predict y.new #' # predict y.new
#' yhat <- predict(cve.obj.s, x.new, khat) #' yhat <- predict(cve.obj.s, x.new, 2)
#' plot(y.new, yhat) #' plot(y.new, yhat)
#'
#' # projection matrix on span(B) #' # projection matrix on span(B)
#' # same as B %*% t(B) since B is semi-orthogonal #' # same as B %*% t(B) since B is semi-orthogonal
#' PB <- B %*% solve(t(B) %*% B) %*% t(B) #' PB <- B %*% solve(t(B) %*% B) %*% t(B)
@ -158,8 +198,15 @@
#' #'
#' @seealso For a detailed description of \code{formula} see #' @seealso For a detailed description of \code{formula} see
#' \code{\link{formula}}. #' \code{\link{formula}}.
#' @references Fertl, L. and Bura, E. (2019), Conditional Variance #'
#' Estimation for Sufficient Dimension Reduction. Working Paper. #' @references
#' [1] Fertl, L. and Bura, E. (2021), Conditional Variance
#' Estimation for Sufficient Dimension Reduction.
#' arXiv:2102.08782
#'
#' [2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
#' Estimation for Sufficient Dimension Reduction.
#' arXiv:2102.13435
#' #'
#' @importFrom stats model.frame #' @importFrom stats model.frame
#' @export #' @export
@ -191,19 +238,27 @@ cve <- function(formula, data, method = "mean", max.dim = 10L, ...) {
#' @inherit cve description #' @inherit cve description
#' #'
#' @param X Design predictor matrix. #' @param X Design predictor matrix.
#' @param Y \eqn{n}-dimensional vector of responces. #' @param Y \eqn{n}-dimensional vector of responses.
#' @param h bandwidth or function to estimate bandwidth, defaults to internaly #' @param h bandwidth or function to estimate bandwidth, defaults to internaly
#' estimated bandwidth. #' estimated bandwidth.
#' @param nObs parameter for choosing bandwidth \code{h} using #' @param nObs parameter for choosing bandwidth \code{h} using
#' \code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied). #' \code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied).
#' @param method specifies the CVE method variation as one of #' @param method This character string specifies the method of fitting. The
#' options are
#' \itemize{ #' \itemize{
#' \item "simple" exact implementation as described in the paper listed #' \item \code{"mean"} method to estimate the mean subspace, see [1].
#' below. #' \item \code{"central"} ensemble method to estimate the central subspace,
#' \item "weighted" variation with addaptive weighting of slices. #' see [2].
#' \item \code{"weighted.mean"} variation of \code{"mean"} method with
#' adaptive weighting of slices, see [1].
#' \item \code{"weighted.central"} variation of \code{"central"} method with
#' adaptive weighting of slices, see [2].
#' } #' }
#' @param func_list a list of functions applied to \code{Y} to form the ensamble #' @param func_list a list of functions applied to \code{Y} used by ECVE
#' CVE for central sub-space estimation. #' (see [2]) for central subspace estimation. The default ensemble are
#' indicator functions of the \eqn{[0, 10], (10, 20], ..., (90, 100]}
#' percent response quantiles. (only relevant if \code{method} is
#' \code{"central"} or \code{"weighted.central"}, ignored otherwise)
#' @param k Dimension of lower dimensional projection, if \code{k} is given #' @param k Dimension of lower dimensional projection, if \code{k} is given
#' only the specified dimension \code{B} matrix is estimated. #' only the specified dimension \code{B} matrix is estimated.
#' @param min.dim lower bounds for \code{k}, (ignored if \code{k} is supplied). #' @param min.dim lower bounds for \code{k}, (ignored if \code{k} is supplied).
@ -214,6 +269,9 @@ cve <- function(formula, data, method = "mean", max.dim = 10L, ...) {
#' @param attempts If \code{V.init} not supplied, the optimization is carried #' @param attempts If \code{V.init} not supplied, the optimization is carried
#' out \code{attempts} times with starting values drawn from the invariant #' out \code{attempts} times with starting values drawn from the invariant
#' measure on the Stiefel manifold (see \code{\link{rStiefel}}). #' measure on the Stiefel manifold (see \code{\link{rStiefel}}).
#' @param nr.proj The number of projection used for projective resampling for
#' multivariate response \eqn{Y} (under active development, ignored for
#' univariate response).
#' @param momentum number of \eqn{[0, 1)} giving the ration of momentum for #' @param momentum number of \eqn{[0, 1)} giving the ration of momentum for
#' eucledian gradient update with a momentum term. \code{momentum = 0} #' eucledian gradient update with a momentum term. \code{momentum = 0}
#' corresponds to normal gradient descend. #' corresponds to normal gradient descend.
@ -230,6 +288,7 @@ cve <- function(formula, data, method = "mean", max.dim = 10L, ...) {
#' computation). #' computation).
#' #'
#' @inherit cve return #' @inherit cve return
#' @inherit cve references
#' #'
#' @examples #' @examples
#' # create B for simulation (k = 1) #' # create B for simulation (k = 1)
@ -262,7 +321,7 @@ cve.call <- function(X, Y,
momentum = 0.0, tau = 1.0, tol = 1e-3, momentum = 0.0, tau = 1.0, tol = 1e-3,
slack = 0.0, gamma = 0.5, slack = 0.0, gamma = 0.5,
V.init = NULL, V.init = NULL,
max.iter = 50L, attempts = 10L, nr.proj = 500L, max.iter = 50L, attempts = 10L, nr.proj = 1L,
logger = NULL logger = NULL
) { ) {
# Determine method with partial matching (shortcuts: "Weight" -> "weighted") # Determine method with partial matching (shortcuts: "Weight" -> "weighted")

10
CVE/R/coef.R
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@ -14,7 +14,7 @@
#' # set dimensions for simulation model #' # set dimensions for simulation model
#' p <- 8 # sample dimension #' p <- 8 # sample dimension
#' k <- 2 # real dimension of SDR subspace #' k <- 2 # real dimension of SDR subspace
#' n <- 200 # samplesize #' n <- 100 # samplesize
#' # create B for simulation #' # create B for simulation
#' b1 <- rep(1 / sqrt(p), p) #' b1 <- rep(1 / sqrt(p), p)
#' b2 <- (-1)^seq(1, p) / sqrt(p) #' b2 <- (-1)^seq(1, p) / sqrt(p)
@ -25,10 +25,10 @@
#' x <- matrix(rnorm(n * p), n, p) #' x <- matrix(rnorm(n * p), n, p)
#' # simulate response variable #' # simulate response variable
#' # y = f(B'x) + err #' # y = f(B'x) + err
#' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2) #' # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.1^2)
#' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.25 * rnorm(100) #' y <- (x %*% b1)^2 + 2 * (x %*% b2) + 0.1 * rnorm(100)
#' # calculate cve for k = 1, ..., 5 #' # calculate cve for k = 2, 3
#' cve.obj <- cve(y ~ x, max.dim = 5) #' cve.obj <- cve(y ~ x, min.dim = 2, max.dim = 3)
#' # get cve-estimate for B with dimensions (p, k = 2) #' # get cve-estimate for B with dimensions (p, k = 2)
#' B2 <- coef(cve.obj, k = 2) #' B2 <- coef(cve.obj, k = 2)
#' #'

6
CVE/R/datasets.R
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@ -200,8 +200,10 @@ rlaplace <- function(n = 1, mu = 0, sd = 1) {
#' location 0, shape-parameter 1, and the scale-parameter is chosen such that #' location 0, shape-parameter 1, and the scale-parameter is chosen such that
#' \eqn{Var(\epsilon) = 0.25}. #' \eqn{Var(\epsilon) = 0.25}.
#' #'
#' @references Fertl, L. and Bura, E. (2019), Conditional Variance #' @references
#' Estimation for Sufficient Dimension Reduction. Working Paper. #' Fertl, L. and Bura, E. (2021), Conditional Variance
#' Estimation for Sufficient Dimension Reduction.
#' arXiv:2102.08782
#' #'
#' @import stats #' @import stats
#' @importFrom stats rnorm rbinom #' @importFrom stats rnorm rbinom

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CVE/R/plot.R
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@ -1,62 +0,0 @@
#' Elbow plot of the loss function.
#'
#' Boxplots of the output \code{L} from \code{\link{cve}} over \code{k} from
#' \code{min.dim} to \code{max.dim}. For given \code{k}, \code{L} corresponds
#' to \eqn{L_n(V, X_i)} where \eqn{V} is the minimizer of \eqn{L_n(V)} where
#' \eqn{V} is an element of a Stiefel manifold (see
#' Fertl, L. and Bura, E. (2019)).
#'
#' @param x an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}.
#' @param ... Pass through parameters to [\code{\link{plot}}] and
#' [\code{\link{lines}}]
#'
#' @examples
#' # create B for simulation
#' B <- cbind(rep(1, 6), (-1)^seq(6)) / sqrt(6)
#'
#' set.seed(21)
#' # creat predictor data x ~ N(0, I_p)
#' X <- matrix(rnorm(600), 100)
#'
#' # simulate response variable
#' # y = f(B'x) + err
#' # with f(x1, x2) = x1^2 + 2 x2 and err ~ N(0, 0.25^2)
#' Y <- (X %*% B[, 1])^2 + 2 * X %*% B[, 2] + rnorm(100, 0, .1)
#'
#' # Create bandwidth estimation function
#' estimate.bandwidth <- function(X, k, nObs) {
#' n <- nrow(X)
#' p <- ncol(X)
#' X_c <- scale(X, center = TRUE, scale = FALSE)
#' 2 * qchisq((nObs - 1) / (n - 1), k) * sum(X_c^2) / (n * p)
#' }
#' # calculate cve with method 'simple' for k = min.dim,...,max.dim
#' cve.obj.simple <- cve(Y ~ X, h = estimate.bandwidth, nObs = sqrt(nrow(X)))
#'
#' # elbow plot
#' plot(cve.obj.simple)
#'
#' @references Fertl, L. and Bura, E. (2019), Conditional Variance
#' Estimation for Sufficient Dimension Reduction. Working Paper.
#'
#' @seealso see \code{\link{par}} for graphical parameters to pass through
#' as well as \code{\link{plot}}, the standard plot utility.
#' @method plot cve
#' @importFrom graphics plot lines points boxplot
#' @export
plot.cve <- function(x, ...) {
L <- c()
k <- c()
for (dr.k in x$res) {
if (class(dr.k) == 'cve.k') {
k <- c(k, as.character(dr.k$k))
L <- c(L, dr.k$L)
}
}
L <- matrix(L, ncol = length(k)) / var(x$Y)
boxplot(L, main = "elbow plot",
xlab = "SDR dimension",
ylab = "Sample loss distribution",
names = k)
}

2
CVE/R/predict.R
View File

@ -1,7 +1,7 @@
#' Predict method for CVE Fits. #' Predict method for CVE Fits.
#' #'
#' Predict response using projected data. The forward model \eqn{g(B' X)} is #' Predict response using projected data. The forward model \eqn{g(B' X)} is
#' estimated with \code{\link{mars}} in the \code{\pkg{mda}} package. #' estimated with \code{\link{mars}} in the \pkg{mda} package.
#' #'
#' @param object an object of class \code{"cve"}, usually, a result of a call to #' @param object an object of class \code{"cve"}, usually, a result of a call to
#' \code{\link{cve}} or \code{\link{cve.call}}. #' \code{\link{cve}} or \code{\link{cve.call}}.

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CVE/inst/doc/CVE_paper.pdf
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26
CVE/man/CVE-package.Rd
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@ -9,7 +9,7 @@
Conditional Variance Estimation (CVE) is a novel sufficient dimension Conditional Variance Estimation (CVE) is a novel sufficient dimension
reduction (SDR) method for regressions satisfying \eqn{E(Y|X) = E(Y|B'X)}, reduction (SDR) method for regressions satisfying \eqn{E(Y|X) = E(Y|B'X)},
where \eqn{B'X} is a lower dimensional projection of the predictors and where \eqn{B'X} is a lower dimensional projection of the predictors and
\eqn{Y} is a univariate responce. CVE, \eqn{Y} is a univariate response. CVE,
similarly to its main competitor, the mean average variance estimation similarly to its main competitor, the mean average variance estimation
(MAVE), is not based on inverse regression, and does not require the (MAVE), is not based on inverse regression, and does not require the
restrictive linearity and constant variance conditions of moment based SDR restrictive linearity and constant variance conditions of moment based SDR
@ -17,7 +17,10 @@ methods. CVE is data-driven and applies to additive error regressions with
continuous predictors and link function. Let \eqn{X} be a real continuous predictors and link function. Let \eqn{X} be a real
\eqn{p}-dimensional covariate vector. We assume that the dependence of \eqn{p}-dimensional covariate vector. We assume that the dependence of
\eqn{Y} and \eqn{X} is modelled by \eqn{Y} and \eqn{X} is modelled by
}
\details{
\deqn{Y = g(B'X) + \epsilon} \deqn{Y = g(B'X) + \epsilon}
where \eqn{X} is independent of \eqn{\epsilon} with positive definite where \eqn{X} is independent of \eqn{\epsilon} with positive definite
variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g} zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
@ -25,10 +28,27 @@ is an unknown, continuous non-constant function,
and \eqn{B = (b_1, ..., b_k)} is and \eqn{B = (b_1, ..., b_k)} is
a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}. a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
Without loss of generality \eqn{B} is assumed to be orthonormal. Without loss of generality \eqn{B} is assumed to be orthonormal.
Further, the extended Ensemble Conditional Variance Estimation (ECVE) is
implemented which is a SDR method in regressions with continuous response and
predictors. ECVE applies to general non-additive error regression models.
\deqn{Y = g(B'X, \epsilon)}
It operates under the assumption that the predictors can be replaced by a
lower dimensional projection without loss of information.It is a
semiparametric forward regression model based exhaustive sufficient dimension
reduction estimation method that is shown to be consistent under mild
assumptions.
} }
\references{ \references{
Fertl, L. and Bura, E. (2019), Conditional Variance [1] Fertl, L. and Bura, E. (2021), Conditional Variance
Estimation for Sufficient Dimension Reduction. Working Paper. Estimation for Sufficient Dimension Reduction.
arXiv:2102.08782
[2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
Estimation for Sufficient Dimension Reduction.
arXiv:2102.13435
} }
\author{ \author{
Daniel Kapla, Lukas Fertl, Bura Efstathia Daniel Kapla, Lukas Fertl, Bura Efstathia

10
CVE/man/coef.cve.Rd
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@ -25,7 +25,7 @@ cve-estimate of \eqn{B} with dimension \eqn{p\times k}{p x k}.
# set dimensions for simulation model # set dimensions for simulation model
p <- 8 # sample dimension p <- 8 # sample dimension
k <- 2 # real dimension of SDR subspace k <- 2 # real dimension of SDR subspace
n <- 200 # samplesize n <- 100 # samplesize
# create B for simulation # create B for simulation
b1 <- rep(1 / sqrt(p), p) b1 <- rep(1 / sqrt(p), p)
b2 <- (-1)^seq(1, p) / sqrt(p) b2 <- (-1)^seq(1, p) / sqrt(p)
@ -36,10 +36,10 @@ set.seed(21)
x <- matrix(rnorm(n * p), n, p) x <- matrix(rnorm(n * p), n, p)
# simulate response variable # simulate response variable
# y = f(B'x) + err # y = f(B'x) + err
# with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2) # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.1^2)
y <- (x \%*\% b1)^2 + 2 * (x \%*\% b2) + 0.25 * rnorm(100) y <- (x \%*\% b1)^2 + 2 * (x \%*\% b2) + 0.1 * rnorm(100)
# calculate cve for k = 1, ..., 5 # calculate cve for k = 2, 3
cve.obj <- cve(y ~ x, max.dim = 5) cve.obj <- cve(y ~ x, min.dim = 2, max.dim = 3)
# get cve-estimate for B with dimensions (p, k = 2) # get cve-estimate for B with dimensions (p, k = 2)
B2 <- coef(cve.obj, k = 2) B2 <- coef(cve.obj, k = 2)

120
CVE/man/cve.Rd
View File

@ -4,7 +4,7 @@
\alias{cve} \alias{cve}
\title{Conditional Variance Estimator (CVE).} \title{Conditional Variance Estimator (CVE).}
\usage{ \usage{
cve(formula, data, method = "simple", max.dim = 10L, ...) cve(formula, data, method = "mean", max.dim = 10L, ...)
} }
\arguments{ \arguments{
\item{formula}{an object of class \code{"formula"} which is a symbolic \item{formula}{an object of class \code{"formula"} which is a symbolic
@ -20,42 +20,17 @@ the environment from which \code{cve} is called.}
\item{method}{This character string specifies the method of fitting. The \item{method}{This character string specifies the method of fitting. The
options are options are
\itemize{ \itemize{
\item "simple" implementation, \item "mean" method to estimate the mean subspace, see [1].
\item "weighted" variation with adaptive weighting of slices. \item "central" ensemble method to estimate the central subspace, see [2].
} \item "weighted.mean" variation of `"mean"` method with adaptive weighting
see Fertl, L. and Bura, E. (2019).} of slices, see [1].
\item "weighted.central" variation of `"central"` method with adaptive
weighting of slices, see [2].
}}
\item{max.dim}{upper bounds for \code{k}, (ignored if \code{k} is supplied).} \item{max.dim}{upper bounds for \code{k}, (ignored if \code{k} is supplied).}
\item{...}{optional parameters passed on to \code{cve.call}. \item{...}{optional parameters passed on to \code{\link{cve.call}}.}
Conditional Variance Estimation (CVE) is a sufficient dimension reduction
(SDR) method for regressions studying \eqn{E(Y|X)}, the conditional
expectation of a response \eqn{Y} given a set of predictors \eqn{X}. This
function provides methods for estimating the dimension and the subspace
spanned by the columns of a \eqn{p\times k}{p x k} matrix \eqn{B} of minimal
rank \eqn{k} such that
\deqn{%
E(Y|X) = E(Y|B'X) %
}
or, equivalently,
\deqn{%
Y = g(B'X) + \epsilon %
}
where \eqn{X} is independent of \eqn{\epsilon} with positive definite
variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
is an unknown, continuous non-constant function, and \eqn{B = (b_1,..., b_k)}
is a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
Both the dimension \eqn{k} and the subspace \eqn{span(B)} are unknown. The
CVE method makes very few assumptions.
A kernel matrix \eqn{\hat{B}}{Bhat} is estimated such that the column space
of \eqn{\hat{B}}{Bhat} should be close to the mean subspace \eqn{span(B)}.
The primary output from this method is a set of orthonormal vectors,
\eqn{\hat{B}}{Bhat}, whose span estimates \eqn{span(B)}.}
} }
\value{ \value{
an S3 object of class \code{cve} with components: an S3 object of class \code{cve} with components:
@ -88,61 +63,108 @@ This is the main function in the \code{CVE} package. It creates objects of
class \code{"cve"} to estimate the mean subspace. Helper functions that class \code{"cve"} to estimate the mean subspace. Helper functions that
require a \code{"cve"} object can then be applied to the output from this require a \code{"cve"} object can then be applied to the output from this
function. function.
Conditional Variance Estimation (CVE) is a sufficient dimension reduction
(SDR) method for regressions studying \eqn{E(Y|X)}, the conditional
expectation of a response \eqn{Y} given a set of predictors \eqn{X}. This
function provides methods for estimating the dimension and the subspace
spanned by the columns of a \eqn{p\times k}{p x k} matrix \eqn{B} of minimal
rank \eqn{k} such that
\deqn{E(Y|X) = E(Y|B'X)}
or, equivalently,
\deqn{Y = g(B'X) + \epsilon}
where \eqn{X} is independent of \eqn{\epsilon} with positive definite
variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
is an unknown, continuous non-constant function, and \eqn{B = (b_1,..., b_k)}
is a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
Both the dimension \eqn{k} and the subspace \eqn{span(B)} are unknown. The
CVE method makes very few assumptions.
A kernel matrix \eqn{\hat{B}}{Bhat} is estimated such that the column space
of \eqn{\hat{B}}{Bhat} should be close to the mean subspace \eqn{span(B)}.
The primary output from this method is a set of orthonormal vectors,
\eqn{\hat{B}}{Bhat}, whose span estimates \eqn{span(B)}.
The method central implements the Ensemble Conditional Variance Estimation
(ECVE) as described in [2]. It augments the CVE method by applying an
ensemble of functions (parameter \code{func_list}) to the response to
estimate the central subspace. This corresponds to the generalization
\deqn{F(Y|X) = F(Y|B'X)}
or, equivalently,
\deqn{Y = g(B'X, \epsilon)}
where \eqn{F} is the conditional cumulative distribution function.
} }
\examples{ \examples{
# set dimensions for simulation model # set dimensions for simulation model
p <- 8 p <- 5
k <- 2 k <- 2
# create B for simulation # create B for simulation
b1 <- rep(1 / sqrt(p), p) b1 <- rep(1 / sqrt(p), p)
b2 <- (-1)^seq(1, p) / sqrt(p) b2 <- (-1)^seq(1, p) / sqrt(p)
B <- cbind(b1, b2) B <- cbind(b1, b2)
# sample size # sample size
n <- 200 n <- 100
set.seed(21) set.seed(21)
# creat predictor data x ~ N(0, I_p) # creat predictor data x ~ N(0, I_p)
x <- matrix(rnorm(n * p), n, p) x <- matrix(rnorm(n * p), n, p)
# simulate response variable # simulate response variable
# y = f(B'x) + err # y = f(B'x) + err
# with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2) # with f(x1, x2) = x1^2 + 2 * x2 and err ~ N(0, 0.25^2)
y <- (x \%*\% b1)^2 + 2 * (x \%*\% b2) + 0.25 * rnorm(100) y <- (x \%*\% b1)^2 + 2 * (x \%*\% b2) + 0.25 * rnorm(n)
# calculate cve with method 'simple' for k unknown in 1, ..., 4
cve.obj.s <- cve(y ~ x, max.dim = 4) # default method 'simple' # calculate cve with method 'mean' for k unknown in 1, ..., 3
cve.obj.s <- cve(y ~ x, max.dim = 2) # default method 'mean'
# calculate cve with method 'weighed' for k = 2 # calculate cve with method 'weighed' for k = 2
cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean') cve.obj.w <- cve(y ~ x, k = 2, method = 'weighted.mean')
# estimate dimension from cve.obj.s B2 <- coef(cve.obj.s, k = 2)
khat <- predict_dim(cve.obj.s)$k
# get cve-estimate for B with dimensions (p, k = khat)
B2 <- coef(cve.obj.s, k = khat)
# get projected X data (same as cve.obj.s$X \%*\% B2) # get projected X data (same as cve.obj.s$X \%*\% B2)
proj.X <- directions(cve.obj.s, k = khat) proj.X <- directions(cve.obj.s, k = 2)
# plot y against projected data # plot y against projected data
plot(proj.X[, 1], y) plot(proj.X[, 1], y)
plot(proj.X[, 2], y) plot(proj.X[, 2], y)
# creat 10 new x points and y according to model # creat 10 new x points and y according to model
x.new <- matrix(rnorm(10 * p), 10, p) x.new <- matrix(rnorm(10 * p), 10, p)
y.new <- (x.new \%*\% b1)^2 + 2 * (x.new \%*\% b2) + 0.25 * rnorm(10) y.new <- (x.new \%*\% b1)^2 + 2 * (x.new \%*\% b2) + 0.25 * rnorm(10)
# predict y.new # predict y.new
yhat <- predict(cve.obj.s, x.new, khat) yhat <- predict(cve.obj.s, x.new, 2)
plot(y.new, yhat) plot(y.new, yhat)
# projection matrix on span(B) # projection matrix on span(B)
# same as B \%*\% t(B) since B is semi-orthogonal # same as B \%*\% t(B) since B is semi-orthogonal
PB <- B \%*\% solve(t(B) \%*\% B) \%*\% t(B) PB <- B \%*\% solve(t(B) \%*\% B) \%*\% t(B)
# cve estimates for B with simple and weighted method # cve estimates for B with mean and weighted method
B.s <- coef(cve.obj.s, k = 2) B.s <- coef(cve.obj.s, k = 2)
B.w <- coef(cve.obj.w, k = 2) B.w <- coef(cve.obj.w, k = 2)
# same as B.s \%*\% t(B.s) since B.s is semi-orthogonal (same vor B.w) # same as B.s \%*\% t(B.s) since B.s is semi-orthogonal (same vor B.w)
PB.s <- B.s \%*\% solve(t(B.s) \%*\% B.s) \%*\% t(B.s) PB.s <- B.s \%*\% solve(t(B.s) \%*\% B.s) \%*\% t(B.s)
PB.w <- B.w \%*\% solve(t(B.w) \%*\% B.w) \%*\% t(B.w) PB.w <- B.w \%*\% solve(t(B.w) \%*\% B.w) \%*\% t(B.w)
# compare estimation accuracy of simple and weighted cve estimate by # compare estimation accuracy of mean and weighted cve estimate by
# Frobenius norm of difference of projections. # Frobenius norm of difference of projections.
norm(PB - PB.s, type = 'F') norm(PB - PB.s, type = 'F')
norm(PB - PB.w, type = 'F') norm(PB - PB.w, type = 'F')
} }
\references{ \references{
Fertl, L. and Bura, E. (2019), Conditional Variance [1] Fertl, L. and Bura, E. (2021), Conditional Variance
Estimation for Sufficient Dimension Reduction. Working Paper. Estimation for Sufficient Dimension Reduction.
arXiv:2102.08782
[2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
Estimation for Sufficient Dimension Reduction.
arXiv:2102.13435
} }
\seealso{ \seealso{
For a detailed description of \code{formula} see For a detailed description of \code{formula} see

79
CVE/man/cve.call.Rd
View File

@ -7,8 +7,8 @@
cve.call( cve.call(
X, X,
Y, Y,
method = "simple", method = c("mean", "weighted.mean", "central", "weighted.central"),
func_list = list(function(x) x), func_list = NULL,
nObs = sqrt(nrow(X)), nObs = sqrt(nrow(X)),
h = NULL, h = NULL,
min.dim = 1L, min.dim = 1L,
@ -22,23 +22,31 @@ cve.call(
V.init = NULL, V.init = NULL,
max.iter = 50L, max.iter = 50L,
attempts = 10L, attempts = 10L,
nr.proj = 1L,
logger = NULL logger = NULL
) )
} }
\arguments{ \arguments{
\item{X}{Design predictor matrix.} \item{X}{Design predictor matrix.}
\item{Y}{\eqn{n}-dimensional vector of responces.} \item{Y}{\eqn{n}-dimensional vector of responses.}
\item{method}{specifies the CVE method variation as one of \item{method}{This character string specifies the method of fitting. The
options are
\itemize{ \itemize{
\item "simple" exact implementation as described in the paper listed \item "mean" method to estimate the mean subspace, see [1].
below. \item "central" ensemble method to estimate the central subspace, see [2].
\item "weighted" variation with addaptive weighting of slices. \item "weighted.mean" variation of `"mean"` method with adaptive weighting
of slices, see [1].
\item "weighted.central" variation of `"central"` method with adaptive
weighting of slices, see [2].
}} }}
\item{func_list}{a list of functions applied to `Y` to form the ensamble \item{func_list}{a list of functions applied to \code{Y} used by ECVE
CVE for central sub-space estimation.} (see [2]) for central subspace estimation. The default ensemble are
indicator functions of the \eqn{[0, 10], (10, 20], ..., (90, 100]}
percent response quantiles. (only relevant if \code{method} is
\code{"central"} or \code{"weighted.central"}, ignored otherwise)}
\item{nObs}{parameter for choosing bandwidth \code{h} using \item{nObs}{parameter for choosing bandwidth \code{h} using
\code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied).} \code{\link{estimate.bandwidth}} (ignored if \code{h} is supplied).}
@ -79,6 +87,10 @@ used as starting value in the optimization. (If supplied,
out \code{attempts} times with starting values drawn from the invariant out \code{attempts} times with starting values drawn from the invariant
measure on the Stiefel manifold (see \code{\link{rStiefel}}).} measure on the Stiefel manifold (see \code{\link{rStiefel}}).}
\item{nr.proj}{The number of projection used for projective resampling for
multivariate response \eqn{Y} (under active development, ignored for
univariate response).}
\item{logger}{a logger function (only for advanced users, slows down the \item{logger}{a logger function (only for advanced users, slows down the
computation).} computation).}
} }
@ -113,6 +125,46 @@ This is the main function in the \code{CVE} package. It creates objects of
class \code{"cve"} to estimate the mean subspace. Helper functions that class \code{"cve"} to estimate the mean subspace. Helper functions that
require a \code{"cve"} object can then be applied to the output from this require a \code{"cve"} object can then be applied to the output from this
function. function.
Conditional Variance Estimation (CVE) is a sufficient dimension reduction
(SDR) method for regressions studying \eqn{E(Y|X)}, the conditional
expectation of a response \eqn{Y} given a set of predictors \eqn{X}. This
function provides methods for estimating the dimension and the subspace
spanned by the columns of a \eqn{p\times k}{p x k} matrix \eqn{B} of minimal
rank \eqn{k} such that
\deqn{E(Y|X) = E(Y|B'X)}
or, equivalently,
\deqn{Y = g(B'X) + \epsilon}
where \eqn{X} is independent of \eqn{\epsilon} with positive definite
variance-covariance matrix \eqn{Var(X) = \Sigma_X}. \eqn{\epsilon} is a mean
zero random variable with finite \eqn{Var(\epsilon) = E(\epsilon^2)}, \eqn{g}
is an unknown, continuous non-constant function, and \eqn{B = (b_1,..., b_k)}
is a real \eqn{p \times k}{p x k} matrix of rank \eqn{k \leq p}{k <= p}.
Both the dimension \eqn{k} and the subspace \eqn{span(B)} are unknown. The
CVE method makes very few assumptions.
A kernel matrix \eqn{\hat{B}}{Bhat} is estimated such that the column space
of \eqn{\hat{B}}{Bhat} should be close to the mean subspace \eqn{span(B)}.
The primary output from this method is a set of orthonormal vectors,
\eqn{\hat{B}}{Bhat}, whose span estimates \eqn{span(B)}.
The method central implements the Ensemble Conditional Variance Estimation
(ECVE) as described in [2]. It augments the CVE method by applying an
ensemble of functions (parameter \code{func_list}) to the response to
estimate the central subspace. This corresponds to the generalization
\deqn{F(Y|X) = F(Y|B'X)}
or, equivalently,
\deqn{Y = g(B'X, \epsilon)}
where \eqn{F} is the conditional cumulative distribution function.
} }
\examples{ \examples{
# create B for simulation (k = 1) # create B for simulation (k = 1)
@ -138,3 +190,12 @@ cve.obj.simple2 <- cve.call(X, Y, k = 1)
coef(cve.obj.simple1, k = 1) coef(cve.obj.simple1, k = 1)
coef(cve.obj.simple2, k = 1) coef(cve.obj.simple2, k = 1)
} }
\references{
[1] Fertl, L. and Bura, E. (2021), Conditional Variance
Estimation for Sufficient Dimension Reduction.
arXiv:2102.08782
[2] Fertl, L. and Bura, E. (2021), Ensemble Conditional Variance
Estimation for Sufficient Dimension Reduction.
arXiv:2102.13435
}

5
CVE/man/dataset.Rd
View File

@ -122,6 +122,7 @@ location 0, shape-parameter 1, and the scale-parameter is chosen such that
} }
\references{ \references{
Fertl, L. and Bura, E. (2019), Conditional Variance Fertl, L. and Bura, E. (2021), Conditional Variance
Estimation for Sufficient Dimension Reduction. Working Paper. Estimation for Sufficient Dimension Reduction.
arXiv:2102.08782
} }

6
CVE/man/directions.cve.Rd
View File

@ -33,11 +33,11 @@ x <- matrix(rnorm(500), 100, 5)
# y = f(B'x) + err # y = f(B'x) + err
# with f(x1) = x1 and err ~ N(0, 0.25^2) # with f(x1) = x1 and err ~ N(0, 0.25^2)
y <- x \%*\% B + 0.25 * rnorm(100) y <- x \%*\% B + 0.25 * rnorm(100)
# calculate cve with method 'simple' for k = 1 # calculate cve with method 'mean' for k = 1
set.seed(21) set.seed(21)
cve.obj.simple <- cve(y ~ x, k = 1, method = 'mean') cve.obj.mean <- cve(y ~ x, k = 1, method = 'mean')
# get projected data for k = 1 # get projected data for k = 1
x.proj <- directions(cve.obj.simple, k = 1) x.proj <- directions(cve.obj.mean, k = 1)
# plot y against projected data # plot y against projected data
plot(x.proj, y) plot(x.proj, y)

57
CVE/man/plot.cve.Rd
View File

@ -1,57 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot.cve}
\alias{plot.cve}
\title{Elbow plot of the loss function.}
\usage{
\method{plot}{cve}(x, ...)
}
\arguments{
\item{x}{an object of class \code{"cve"}, usually, a result of a call to
\code{\link{cve}} or \code{\link{cve.call}}.}
\item{...}{Pass through parameters to [\code{\link{plot}}] and
[\code{\link{lines}}]}
}
\description{
Boxplots of the output \code{L} from \code{\link{cve}} over \code{k} from
\code{min.dim} to \code{max.dim}. For given \code{k}, \code{L} corresponds
to \eqn{L_n(V, X_i)} where \eqn{V} is the minimizer of \eqn{L_n(V)} where
\eqn{V} is an element of a Stiefel manifold (see
Fertl, L. and Bura, E. (2019)).
}
\examples{
# create B for simulation
B <- cbind(rep(1, 6), (-1)^seq(6)) / sqrt(6)
set.seed(21)
# creat predictor data x ~ N(0, I_p)
X <- matrix(rnorm(600), 100)
# simulate response variable
# y = f(B'x) + err
# with f(x1, x2) = x1^2 + 2 x2 and err ~ N(0, 0.25^2)
Y <- (X \%*\% B[, 1])^2 + 2 * X \%*\% B[, 2] + rnorm(100, 0, .1)
# Create bandwidth estimation function
estimate.bandwidth <- function(X, k, nObs) {
n <- nrow(X)
p <- ncol(X)
X_c <- scale(X, center = TRUE, scale = FALSE)
2 * qchisq((nObs - 1) / (n - 1), k) * sum(X_c^2) / (n * p)
}
# calculate cve with method 'simple' for k = min.dim,...,max.dim
cve.obj.simple <- cve(Y ~ X, h = estimate.bandwidth, nObs = sqrt(nrow(X)))
# elbow plot
plot(cve.obj.simple)
}
\references{
Fertl, L. and Bura, E. (2019), Conditional Variance
Estimation for Sufficient Dimension Reduction. Working Paper.
}
\seealso{
see \code{\link{par}} for graphical parameters to pass through
as well as \code{\link{plot}}, the standard plot utility.
}

2
CVE/man/predict.cve.Rd
View File

@ -21,7 +21,7 @@ prediced respone(s) for \code{newdata}.
} }
\description{ \description{
Predict response using projected data. The forward model \eqn{g(B' X)} is Predict response using projected data. The forward model \eqn{g(B' X)} is
estimated with \code{\link{mars}} in the \code{\pkg{mda}} package. estimated with \code{\link{mars}} in the \pkg{mda} package.
} }
\examples{ \examples{
# create B for simulation # create B for simulation

2
CVE/man/predict_dim.Rd
View File

@ -51,7 +51,7 @@ x <- matrix(rnorm(500), 100)
y <- x \%*\% B + 0.25 * rnorm(100) y <- x \%*\% B + 0.25 * rnorm(100)
# Calculate cve for unknown k between min.dim and max.dim. # Calculate cve for unknown k between min.dim and max.dim.
cve.obj.simple <- cve(y ~ x) cve.obj.simple <- cve(y ~ x)
predict_dim(cve.obj.simple) predict_dim(cve.obj.simple)

235
cve_tensorflow.R
View File

@ -1,235 +0,0 @@
library(CVE)
library(reticulate)
library(tensorflow)
#' Null space basis of given matrix `V`
#'
#' @param V `(p, q)` matrix
#' @return Semi-orthogonal `(p, p - q)` matrix spaning the null space of `V`.
#' @keywords internal
#' @export
null <- function(V) {
tmp <- qr(V)
set <- if(tmp$rank == 0L) seq_len(ncol(V)) else -seq_len(tmp$rank)
return(qr.Q(tmp, complete = TRUE)[, set, drop = FALSE])
}
subspace_dist <- function(A, B) {
P <- A %*% solve(t(A) %*% A, t(A))
Q <- B %*% solve(t(B) %*% B, t(B))
norm(P - Q, 'F') / sqrt(ncol(A) + ncol(B))
}
estimate.bandwidth <- function (X, k, nObs = sqrt(nrow(X)), version = 1L) {
n <- nrow(X)
p <- ncol(X)
X_c <- scale(X, center = TRUE, scale = FALSE)
if (version == 1) {
(2 * sum(X_c^2) / (n * p)) * (1.2 * n^(-1 / (4 + k)))^2
} else if (version == 2) {
2 * qchisq((nObs - 1) / (n - 1), k) * sum(X_c^2) / (n * p)
} else {
stop("Unknown version.")
}
}
tf_Variable <- function(obj, dtype = "float32", ...) {
tf$Variable(obj, dtype = dtype, ...)
}
tf_constant <- function(obj, dtype = "float32", ...) {
tf$constant(obj, dtype = dtype, ...)
}
cve.tf <- function(X, Y, k, h = estimate.bandwidth(X, k, sqrt(nrow(X))),
V.init = NULL, optimizer_initialier = tf$optimizers$RMSprop, attempts = 10L,
nr.projections = nrow(X)^(3 / 2),
sd_noise = 0, method = c("simple", "weighted")
) {
method <- match.arg(method)
`-0.5` <- tf_constant(-0.5)
`1` <- tf_constant(1)
`2` <- tf_constant(2)
n <- nrow(X)
p <- ncol(X)
k <- as.integer(k)
q <- p - k
if (!is.matrix(Y))
Y <- as.matrix(Y)
# Projective resampling.
if (ncol(Y) > 1L) {
R <- matrix(rnorm(ncol(Y) * nr.projections), ncol(Y))
R <- t(t(R) / sqrt(colSums(R^2)))
Y <- Y %*% R
}
X <- tf_constant(scale(X))
Y <- tf_constant(scale(Y))
I <- tf_constant(diag(1, p))
h <- tf_Variable(h)
loss <- tf_function(function(V) {
Q <- I - tf$matmul(V, V, transpose_b = TRUE)
if (sd_noise > 0)
XQ <- tf$matmul(X + tf$random$normal(list(n, p), stddev = sd_noise), Q)
else
XQ <- tf$matmul(X, Q)
S <- tf$matmul(XQ, XQ, transpose_b = TRUE)
d <- tf$linalg$diag_part(S)
D <- tf$reshape(d, list(n, 1L)) + tf$reshape(d, list(1L, n)) - `2` * S
K <- tf$exp((`-0.5` / h) * tf$pow(D, 2L))
w <- tf$reduce_sum(K, 1L, keepdims = TRUE)
y1 <- tf$divide(tf$matmul(K, Y), w)
y2 <- tf$divide(tf$matmul(K, tf$pow(Y, 2L)), w)
if (method == "simple") {
l <- tf$reduce_mean(y2 - tf$pow(y1, 2L))
} else {# weighted
w <- w - `1`
w <- w / tf$reduce_sum(w)
l <- tf$reduce_sum(w * (y2 - tf$pow(y1, 2L)))
l <- l / tf$cast(tf$shape(Y)[2], "float32")
}
l
})
if (is.null(V.init))
V.init <- qr.Q(qr(matrix(rnorm(p * q), p, q)))
else
attempts <- 1L
V <- tf_Variable(V.init, constraint = function(w) { tf$linalg$qr(w)$q })
min.loss <- Inf
for (attempt in seq_len(attempts)) {
optimizer = optimizer_initialier()
out <- tf$while_loop(
cond = tf_function(function(i, L) i < 400L),
body = tf_function(function(i, L) {
with(tf$GradientTape() %as% tape, {
tape$watch(V)
L <- loss(V)
})
grad <- tape$gradient(L, V)
optimizer$apply_gradients(list(list(grad, V)))
list(i + 1L, L)
}),
loop_vars = list(tf_constant(0L, "int32"), tf_constant(Inf))
)
if (as.numeric(out[[2]]) < min.loss) {
min.loss <- as.numeric(out[[2]])
min.V <- as.matrix(V)
}
V$assign(qr.Q(qr(matrix(rnorm(p * q), p, q))))
}
list(B = null(min.V), V = min.V, loss = min.loss)
}
# ds <- dataset(1)
# out <- cve.call2(ds$X, ds$Y, ncol(ds$B))
plot.sim <- function(sim) {
name <- deparse(substitute(sim))
ssd <- sapply(sim, function(s) subspace_dist(s$B.true, s$B.est))
print(summary(ssd))
h <- hist(ssd, freq = FALSE, breaks = seq(0, 1, 0.1), main = name,
xlab = "Subspace Distance")
lines(density(ssd, from = 0, to = 1))
stat <- c(Median = median(ssd), Mean = mean(ssd))
abline(v = stat, lty = 2)
text(stat, max(h$density), names(stat),
pos = if(diff(stat) > 0) c("2", "4") else c("4", "2"))
}
multivariate.dataset <- function(dataset = 1, n = 100, p = 6, q = 4) {
CVE <- getNamespace('CVE')
X <- matrix(rnorm(n * p), n, p)
if (dataset == 1) {
Delta <- diag(1, q, q)
Delta[1, 2] <- Delta[2, 1] <- -0.5
epsilon <- CVE$rmvnorm(n, sigma = Delta)
B <- matrix(0, p, q)
B[1, 1] <- 1
B[2, 2] <- 2 / sqrt(5)
B[3, 2] <- 1 / sqrt(5)
Y <- X %*% B + epsilon
B <- B[, 1:2]
}
if (dataset == 2) {
B <- matrix(c(0.8, 0.6, 0, 0, 0, 0))
eps <- matrix(0, n, q)
Delta <- diag(1, q, q)
for(i in 1:n) {
Delta[1, 2] <- Delta[2, 1] <- sin(X[i, ] %*% B)
eps[i, ] <- CVE$rmvnorm(1, sigma = Delta)
}
Y<-cbind(exp(eps[, 1]), eps[, 2:4])
}
list(X = X, Y = Y, B = B)
}
set.seed(42)
reps <- 5L
sim.cve.m <- vector("list", reps)
sim.cve.c <- vector("list", reps)
sim.cve.wm <- vector("list", reps)
sim.cve.wc <- vector("list", reps)
sim.tf1 <- vector("list", reps)
sim.tf2 <- vector("list", reps)
start <- Sys.time()
for (i in 1:reps) {
# ds <- dataset(1)
ds <- multivariate.dataset(2, n = 400)
sim.cve.m[[i]] <- list(
B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "mean"), ncol(ds$B)),
B.true = ds$B
)
sim.cve.c[[i]] <- list(
B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "central"), ncol(ds$B)),
B.true = ds$B
)
sim.cve.wm[[i]] <- list(
B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "weighted.mean"), ncol(ds$B)),
B.true = ds$B
)
sim.cve.wc[[i]] <- list(
B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "weighted.central"), ncol(ds$B)),
B.true = ds$B
)
# sim.tf1[[i]] <- list(
# B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
# optimizer_initialier = tf$optimizers$Adam)$B,
# B.true = ds$B
# )
# sim.tf2[[i]] <- list(
# B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
# optimizer_initialier = tf$optimizers$Adam,
# method = "weighted")$B,
# B.true = ds$B
# )
cat(sprintf("\r%4d/%d -", i, reps), format(Sys.time() - start), '\n')
}
# pdf('subspace_comp.pdf')
plot.sim(sim.cve)
plot.sim(sim.tf1)
plot.sim(sim.tf2)
par(mfrow = c(2, 2))
plot.sim(sim.cve.m)
plot.sim(sim.cve.c)
plot.sim(sim.cve.wm)
plot.sim(sim.cve.wc)
# dev.off()