mdpeer: Graph-Constrained Regression with Enhanced Regularization Parameters Selection (original) (raw)
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.
Version: | 1.0.1 |
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Depends: | R (≥ 3.3.3) |
Imports: | reshape2, ggplot2, nlme, boot, nloptr, rootSolve, psych, magic, glmnet |
Suggests: | knitr, rmarkdown |
Published: | 2017-05-30 |
DOI: | 10.32614/CRAN.package.mdpeer |
Author: | Marta Karas [aut, cre], Damian Brzyski [ctb], Jaroslaw Harezlak [ctb] |
Maintainer: | Marta Karas <marta.karass at gmail.com> |
License: | GPL-2 |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | mdpeer results |
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