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plsdof (version 0.2-1)

Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression

Description

The plsdof package provides Degrees of Freedom estimates and approximate confidence intervals for Partial Least Squares Regression. The package also allows model selection based on various information criteria (aic, bic, gmdl) and based on cross-validation.

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Version

Install

install.packages('plsdof')

Monthly Downloads

371

Version

0.2-1

License

GPL (>= 2)

Maintainer

Nicole Kraemer

Last Published

June 3rd, 2010

Functions in plsdof (0.2-1)

information.criteria

Information criteria
dnormalize

Derivative of normalization function
aic

Akaike Information Criterion
pls.model

Partial Least Squares
plsdof-package

Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression
vvtz

Projectin operator
normalize

Normalization of vectors
dvvtz

First derivative of the projection operator
bic

Bayesian information criterion
linear.pls

Linear Partial Least Squares Fit
coef.plsdof

Regression coefficients
pls.dof

Computation of the Degrees of Freedom
krylov

Krylov sequence
first.local.minimum

Index of the first local minimum.
dA

Derivative of normalization function
tr

Trace of a matrix
kernel.pls.fit

Kernel Partial Least Squares Fit
pls.ic

Model selection for Partial Least Squares based on information criteria
pls.cv

Model selection for Partial Least Squares based on cross-validation
vcov.plsdof

Variance-covariance matrix
gmdl

Generalized minimum description length
benchmark.pls

Comparison of model selection criteria for Partial Least Squares Regression.