`R/plsdof-package.R`

`plsdof-package.Rd`

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.

Package: | plsdof |

Type: | Package |

Version: | 0.2-9 |

Date: | 2019-31-01 |

License: | GPL (>=2) |

LazyLoad: | yes |

Kraemer, N., Sugiyama M. (2011). "The Degrees of Freedom of Partial Least Squares Regression". Journal of the American Statistical Association 106 (494) 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

Nicole Kraemer, Mikio L. Braun

Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>

# 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))# 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