mdpeer: Graph-Constrained Regression with Enhanced Regularization Parameters Selection
Version 1.0.1

Provides graph-constrained regression methods in which regularization parameters are selected automatically via estimation of equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially Empirical Eigenvectors for Regression) method employs a penalty term being a linear combination of graph-originated and ridge-originated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution; a graph-originated penalty term allows imposing similarity between coefficients based on graph information given whereas additional ridge-originated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graph-originated penalty matrix and yields plausible results in situations when graph information is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors for Regression with constant) method utilizes addition of a diagonal matrix multiplied by a predefined (small) scalar to handle the non-invertibility of a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix.

Package details

AuthorMarta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb]
Date of publication2017-05-30 04:44:40 UTC
MaintainerMarta Karas <marta.karass@gmail.com>
LicenseGPL-2
Version1.0.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("mdpeer")

Try the mdpeer package in your browser

Any scripts or data that you put into this service are public.

mdpeer documentation built on May 31, 2017, 5:21 a.m.