# RXshrink v1.4.3

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## Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression Methods

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.

## Functions in RXshrink

Name | Description | |

correct.signs | Normal-Theory Maximum Likelihood Estimation of Beta Coefficients with "Correct" Signs | |

aug.lars | Maximum Likelihood Estimation of Effects in Least Angle Regression | |

haldport | Portland Cement benchmark of Hald(1952) | |

MLboot | Calculate Bootstrap distribution of Unrestricted Maximum Likelihood (ML) point-estimates for a Linear Model. | |

RXshrink-internal | Internal RXshrink functions | |

MLtrue | Simulate data for Linear Models with known Parameter values and Normal Errors | |

RXshrink-package | Maximum Likelihood (ML) Shrinkage using Generalized Ridge or Least Angle Regression Methods | |

kofm | k-Multipliers and delta-Factors for unr.ridge() Shrinkage. | |

MLhist | Plot method for MLboot objects | |

RXpredict | Predictions from Models fit using RXshrink Generalized Ridge Estimation Methods. | |

longley2 | Art Hoerl's update of the infamous Longley(1967) benchmark dataset | |

plot.unr.biv | Plot method for unr.biv objects | |

mpg | Hocking(1976) Miles Per Gallon benchmark dataset | |

mofk | m-Extents of Shrinkage used in unr.ridge() Calculations. | |

unr.biv | Specify pairs of GRR Coefficient Estimates for display in Bivariate Confidence Regions | |

unr.ridge | Unrestricted Maximum Likelihood (ML) Shrinkage using a Piecewise Linear-Spline PATH | |

plot.aug.lars | Plot method for aug.lars objects | |

plot.RXpredict | Plot method for RXpredict objects | |

qm.ridge | Restricted (2-parameter) Maximum Likelihood Shrinkage in Regression | |

tycobb | Ty Cobb batting statistics for 1905--1928 with Carl Morris' 2-piece Spline term. | |

plot.unr.ridge | Plot method for unr.ridge objects | |

plot.qm.ridge | Plot method for qm.ridge objects | |

uc.lars | Maximum Likelihood Least Angle Regression on Uncorrelated X-Components | |

MLcalc | Calculate Unrestricted Maximum Likelihood (ML) point-estimates for a Linear Model that are either Unbiased (OLS) or Optimally Biased under Normal-distribution theory. | |

unr.aug | Augment calculations performed by unr.ridge() to prepare for display of eliptical confidence regions for pairs of biased coefficient estimates using plot.unr.biv() | |

plot.uc.lars | Plot method for uc.lars objects | |

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## Last month downloads

## Details

Date | 2020-11-01 |

License | GPL-2 |

URL | https://www.R-project.org , http://localcontrolstatistics.org |

NeedsCompilation | no |

Packaged | 2020-10-30 01:06:18 UTC; bobo |

Repository | CRAN |

Date/Publication | 2020-11-01 13:30:02 UTC |

depends | ellipse , lars , R (>= 3.5.0) |

Contributors | Bob Obenchain |

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