R/plsdof-package.R

In plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

#' Degrees of Freedom and Statistical Inference for Partial Least Squares
#' Regression
#' 
#' The plsdof package provides Degrees of Freedom estimates for Partial Least
#' Squares (PLS) Regression.
#' 
#' Model selection for PLS is based on various information criteria (aic, bic,
#' gmdl) or on cross-validation. Estimates for the mean and covariance of the
#' PLS regression coefficients are available. They allow the construction of
#' approximate confidence intervals and the application of test procedures.
#' 
#' Further, cross-validation procedures for Ridge Regression and Principal
#' Components Regression are available.
#' 
#' \tabular{ll}{ Package: \tab plsdof\cr Type: \tab Package\cr Version: \tab
#' 0.2-9\cr Date: \tab 2019-31-01\cr License: \tab GPL (>=2)\cr LazyLoad: \tab
#' yes\cr }
#' 
#' @name plsdof-package
#' @aliases plsdof-package plsdof
#' 
#' @docType package
#' @author Nicole Kraemer, Mikio L. Braun
#' 
#' Maintainer: Frederic Bertrand <frederic.bertrand@@utt.fr.fr>
#' @seealso \code{\link{pls.model}}, \code{\link{pls.cv}}, \code{\link{pls.ic}}
#' @references
#' 
#' Kraemer, N., Sugiyama M. (2011). "The Degrees of Freedom of Partial Least
#' Squares Regression". Journal of the American Statistical Association 106
#' (494) \url{https://www.tandfonline.com/doi/abs/10.1198/jasa.2011.tm10107}
#' 
#' Kraemer, N., Braun, M.L. (2007) "Kernelizing PLS, Degrees of Freedom, and
#' Efficient Model Selection", Proceedings of the 24th International Conference
#' on Machine Learning, Omni Press, 441 - 448
#' @keywords package
#' 
#' @import MASS
#' @importFrom graphics plot
#' @importFrom stats coef cor sd
#' 
#' 
#' @examples
#' 
#' # Boston Housing data
#' data(Boston)
#' X<-as.matrix(Boston[,-14])
#' y<-as.vector(Boston[,14])
#' 
#' # compute PLS coefficients for the first 5 components and plot Degrees of Freedom
#' 
#' my.pls1<-pls.model(X,y,m=5,compute.DoF=TRUE)
#' 
#' plot(0:5,my.pls1$DoF,pch="*",cex=3,xlab="components",ylab="DoF",ylim=c(0,14))
#' 
#' # add naive estimate
#' lines(0:5,1:6,lwd=3)
#' 
#' # model selection with the Bayesian Information criterion
#' 
#' mypls2<-pls.ic(X,y,criterion="bic")
#' 
#' # model selection based on cross-validation. 
#' # returns the estimated covariance matrix of the regression coefficients
#' 
#' mypls3<-pls.cv(X,y,compute.covariance=TRUE)
#' my.vcov<-vcov(mypls3)
#' my.sd<-sqrt(diag(my.vcov)) # standard deviation of the regression coefficients
#' 
#' 
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