mdpeer: 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.
| Version: |
1.0.1 |
| 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 |
Documentation:
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