| Title: | Broken Adaptive Ridge Regression with Cyclops |
|---|---|
| Description: | Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>. |
| Authors: | Marc A. Suchard [aut, cre], Eric Kawaguchi [aut], Ning Li [aut], Gang Li [aut], Observational Health Data Sciences and Informatics [cph] |
| Maintainer: | Marc A. Suchard <[email protected]> |
| License: | Apache License 2.0 |
| Version: | 1.0.0 |
| Built: | 2024-10-29 04:31:53 UTC |
| Source: | https://github.com/cran/BrokenAdaptiveRidge |
createBarPrior creates a BAR Cyclops prior object for use with fitCyclopsModel.
createBarPrior( penalty = "bic", exclude = c(), forceIntercept = FALSE, fitBestSubset = FALSE, initialRidgeVariance = 10000, tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06, delta = 0 )createBarPrior( penalty = "bic", exclude = c(), forceIntercept = FALSE, fitBestSubset = FALSE, initialRidgeVariance = 10000, tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06, delta = 0 )
penalty |
Specifies the BAR penalty; possible values are 'BIC' or 'AIC' or a numeric value |
exclude |
A vector of numbers or covariateId names to exclude from prior |
forceIntercept |
Logical: Force intercept coefficient into regularization |
fitBestSubset |
Logical: Fit final subset with no regularization |
initialRidgeVariance |
Numeric: variance used for algorithm initiation |
tolerance |
Numeric: maximum abs change in coefficient estimates from successive iterations to achieve convergence |
maxIterations |
Numeric: maxium iterations to achieve convergence |
threshold |
Numeric: absolute threshold at which to force coefficient to 0 |
delta |
Numeric: change from 2 in ridge norm dimension |
A BAR Cyclops prior object of class inheriting from
"cyclopsPrior" for use with fitCyclopsModel.
prior <- createBarPrior(penalty = "bic")prior <- createBarPrior(penalty = "bic")
createFastBarPrior creates a fastBAR Cyclops prior object for use with fitCyclopsModel.
createFastBarPrior( penalty = 0, exclude = c(), forceIntercept = FALSE, fitBestSubset = FALSE, initialRidgeVariance = 10000, tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06 )createFastBarPrior( penalty = 0, exclude = c(), forceIntercept = FALSE, fitBestSubset = FALSE, initialRidgeVariance = 10000, tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06 )
penalty |
Specifies the BAR penalty |
exclude |
A vector of numbers or covariateId names to exclude from prior |
forceIntercept |
Logical: Force intercept coefficient into regularization |
fitBestSubset |
Logical: Fit final subset with no regularization |
initialRidgeVariance |
Numeric: variance used for algorithm initiation |
tolerance |
Numeric: maximum abs change in coefficient estimates from successive iterations to achieve convergence |
maxIterations |
Numeric: maximum iterations to achieve convergence |
threshold |
Numeric: absolute threshold at which to force coefficient to 0 |
A BAR Cyclops prior object of class inheriting from
"cyclopsPrior" for use with fitCyclopsModel.
nobs = 500; ncovs = 100 prior <- createFastBarPrior(penalty = log(ncovs), initialRidgeVariance = 1 / log(ncovs))nobs = 500; ncovs = 100 prior <- createFastBarPrior(penalty = log(ncovs), initialRidgeVariance = 1 / log(ncovs))