Provides graphconstrained 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 graphoriginated and ridgeoriginated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution; a graphoriginated penalty term allows imposing similarity between coefficients based on graph information given whereas additional ridgeoriginated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graphoriginated 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 noninvertibility of a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs variablereduction procedure to handle the noninvertibility of a graph Laplacian matrix.
Package details 


Author  Marta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb] 
Date of publication  20170530 04:44:40 UTC 
Maintainer  Marta Karas <[email protected]> 
License  GPL2 
Version  1.0.1 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.