plsdof: 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.

AuthorNicole Kraemer, Mikio L. Braun
Date of publication2014-09-04 15:41:41
MaintainerNicole Kraemer <kraemer_r_packages@yahoo.de>
LicenseGPL (>= 2)
Version0.2-7

View on CRAN

Functions

benchmark.pls Man page
benchmark.regression Man page
coef.plsdof Man page
compute.lower.bound Man page
dA Man page
dnormalize Man page
dvvtz Man page
first.local.minimum Man page
information.criteria Man page
kernel.pls.fit Man page
krylov Man page
linear.pls.fit Man page
normalize Man page
pcr Man page
pcr.cv Man page
pls.cv Man page
plsdof Man page
pls.dof Man page
plsdof-package Man page
pls.ic Man page
pls.model Man page
ridge.cv Man page
tr Man page
vcov.plsdof Man page
vvtz Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.