The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression.

## Details

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

## References

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

pls.model, pls.cv, pls.ic

## Author

Nicole Kraemer, Mikio L. Braun

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

## 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))

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