Package: RXshrink
Title: Maximum Likelihood Shrinkage using Generalized Ridge or Least
Angle Regression Methods
Version: 1.5
Date: 2020-12-15
Author: Bob Obenchain
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: R (>= 3.5.0), lars, ellipse
Description: Functions are provided to calculate and display ridge TRACE diagnostics for
a variety of shrinkage Paths. TRACEs identify the m-Extent of shrinkage most likely,
under Normal-theory, to produce optimally biased estimates of beta-coefficients with
minimum MSE Risk. The unr.ridge() function implements the "Unrestricted Path"
introduced in Obenchain (2020) <arXiv:2005.14291>. This Shrinkage-Path is more
efficient than the Paths used by the qm.ridge(), aug.lars() and uc.lars() functions.
Optimally biased predictions can be made using RXpredict() for all six types of
RXshrink linear model estimation methods. Functions MLboot(), MLcalc(), MLhist() and
MLtrue() provide insights into the true bias and MSE risk characteristics of non-linear
Shrinkage estimators. Functions unr.aug() and unr.biv() augment the calculations made
by unr.ridge() to provide plots of the bivariate confidence ellipses corresponding to
any of the p*(p-1) possible pairs of shrunken regression coefficients. The correct.signs()
function provides estimates with "correct" numerical signs when ill-conditioned (nearly
multicollinear) models yield OLS estimates that disagree with the signs of the observed
correlations between the y-outcome and the selected x-predictor variables. Finally, the
YonX() function displays Shrinkage statistics and graphics for "simple" linear regression
(p = 1) models.
License: GPL-2
URL: https://www.R-project.org , http://localcontrolstatistics.org
NeedsCompilation: no
Packaged: 2020-12-15 23:08:48 UTC; bobo
Repository: CRAN
Date/Publication: 2020-12-16 05:30:09 UTC

Description: Functions are provided to calculate and display ridge TRACE diagnostics for a
wide variety of alternative shrinkage Paths. While all methods focus on Maximum Likelihood
estimation of unknown true effects under Normal-distribution theory, some estimates are
modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality
of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE
Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.)
The unr.ridge() function implements the "Unrestricted Path" introduced in Obenchain (2020)
<arXiv:2005.14291>. This "new" p-parameter Shrinkage-Path is more efficient than the Paths
used by qm.ridge(), aug.lars() and uc.lars(). Functions unr.aug() and unr.biv() augment the
calculations made by unr.ridge() to provide plots of the bivariate confidence ellipses
corresponding to any of the p*(p-1) possible pairs of shrunken regression coefficients.