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.

Author | Nicole Kraemer, Mikio L. Braun |

Date of publication | 2014-09-04 15:41:41 |

Maintainer | Nicole Kraemer <kraemer_r_packages@yahoo.de> |

License | GPL (>= 2) |

Version | 0.2-7 |

**benchmark.pls:** Comparison of model selection criteria for Partial Least...

**benchmark.regression:** Comparison of Partial Least Squares Regression, Principal...

**coef.plsdof:** Regression coefficients

**compute.lower.bound:** Lower bound for the Degrees of Freedom

**dA:** Derivative of normalization function

**dnormalize:** Derivative of normalization function

**dvvtz:** First derivative of the projection operator

**first.local.minimum:** Index of the first local minimum.

**information.criteria:** Information criteria

**kernel.pls.fit:** Kernel Partial Least Squares Fit

**krylov:** Krylov sequence

**linear.pls.fit:** Linear Partial Least Squares Fit

**normalize:** Normalization of vectors

**pcr:** Principal Components Regression

**pcr.cv:** Model selection for Princinpal Components regression based on...

**pls.cv:** Model selection for Partial Least Squares based on...

**pls.dof:** Computation of the Degrees of Freedom

**plsdof-package:** Degrees of Freedom and Statistical Inference for Partial...

**pls.ic:** Model selection for Partial Least Squares based on...

**pls.model:** Partial Least Squares

**ridge.cv:** Ridge Regression.

**tr:** Trace of a matrix

**vcov.plsdof:** Variance-covariance matrix

**vvtz:** Projectin operator

plsdof

plsdof/inst

plsdof/inst/CITATION

plsdof/inst/ChangeLog

plsdof/NAMESPACE

plsdof/R

plsdof/R/pls.cv.R
plsdof/R/compute.lower.bound.R
plsdof/R/dA.R
plsdof/R/pls.ic.R
plsdof/R/vcov.plsdof.R
plsdof/R/dnormalize.R
plsdof/R/coef.plsdof.R
plsdof/R/tr.r

plsdof/R/linear.pls.fit.R
plsdof/R/first.local.minimum.R
plsdof/R/krylov.r

plsdof/R/ridge.cv.R
plsdof/R/benchmark.pls.R
plsdof/R/kernel.pls.fit.R
plsdof/R/benchmark.regression.R
plsdof/R/pls.model.R
plsdof/R/vvtz.r

plsdof/R/pls.dof.R
plsdof/R/normalize.R
plsdof/R/information.criteria.R
plsdof/R/dvvtz.r

plsdof/R/pcr.R
plsdof/R/pcr.cv.R
plsdof/MD5

plsdof/DESCRIPTION

plsdof/man

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