Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression


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Documentation for package ‘plsdof’ version 0.1-1

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plsdof-package Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression
aic Akaike information criterion
bic Bayesian information criterion
coef.plsdof Regression coefficients
dA Derivative of normalization function
dnormalize Derivative of normalization function
dvvtz First derivative of the projection operator
gmdl Generalized minimum description length
information.criteria Information criteria
kernel.pls Kernel Partial Least Squares
kernel.pls.cv Cross-validation for Kernel Partial Least Squares
kernel.pls.fit Kernel Partial Least Squares fit
kernel.pls.ic Model selection for Kernel Partial Least Squares based on information criteria
linear.pls Linear Partial Least Squares
myrange.X Scaling of predictor variables
normalize Normalization of vectors
plsdof Degrees of Freedom and Confidence Intervals for Partial Least Squares Regression
vcov.plsdof Variance-covariance matrix
vvtz Projectin operator
where.max Compute argmax of a vector
X2kernel Computation of the kernel matrix