Graph-Constrained Regression with Enhanced Regularization Parameters Selection

Performs graph-constrained regularization in which regularization parameters are selected with the use of a known fact of equivalence between penalized regression and Linear Mixed Model solutions. Provides implementation of three different regression methods where graph-constraints among coefficients are accounted for. 'crPEER' (Partially Empirical Eigenvectors for Regression with Constant Ridge, Constant Ridge PEER) method utilizes additional Ridge term to handle the non-invertibility of a graph Laplacian matrix. 'vrPEER' (Variable Reduction PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix. Finally, 'RidgePEER' 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. Notably, in 'RidgePEER' method 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 or when it is unclear whether connectivities represented by a graph reflect similarities among corresponding coefficients.


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0.1.0 by Marta Karas, 6 months ago

Browse source code at

Authors: Marta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb]

Documentation:   PDF Manual  

GPL-2 license

Imports MASS, glmgraph, magic, reshape2, ggplot2, scalreg, glmnet, nlme, nloptr

Suggests knitr, rmarkdown

See at CRAN