Graph-Constrained Regression with Enhanced Regularization Parameters Selection

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.


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1.0.1 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 reshape2, ggplot2, nlme, boot, nloptr, rootSolve, psych, magic, glmnet

Suggests knitr, rmarkdown

See at CRAN