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

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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

View on CRAN

Man pages

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

Files in this package

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