Numerical experiments for evaluating the "multiple regression with adaptive shrinkage" (MR.ASH) method and other linear regression methods that are well suited for large-scale data sets.
This workflow is for reproducing the results used in the manuscript
MR.ASH: A Novel Variational Empirical Bayes Approach to Multiple Linear Regression.
See Mr.ASH Vignette
The proposed VEB (Variational Empirical Bayes) approach can be implemented via our companion package mr.ash.alpha,
which can be found in MR.ASH github.
To install the latest version of the R package mr.ash.alpha from MR.ASH github, clone or
download the git repository, then use the install_local function from
devtools, replacing "path/to/repo" with the appropriate directory path:
devtools::install_local("path/to/repo")Also, one can install the package via
devtools::install_github("stephenslab/mr.ash.alpha")The list of competitors is as follows.
# Penalized linear regression based on Ridge, Lasso, Elastic Net penalties, fitted by CV (Ridge, Lass, E-NET)
install.packages("glmnet")
# Penalized linear regression based on SCAD, MCP penalties, fitted by CV (SCAD, MCP)
install.packages("ncvreg")
# Penalized linear regression based on L0 penalty (or L0+L1, L0+L2), fitted by CV (L0Learn)
install.packages("L0Learn")
# Bayesian linear regression based on additive single effect model, fitted by VB (SuSiE)
install_github("stephenslab/susieR")
# Bayesian linear regression based on hieararchical spike-and-slab prior, fitted by MCMC (BayesB, BLasso)
install.packages("BGLR")
# Bayesian linear regression based on hieararchical spike-and-slab prior, fitted by VB and discrete BMA (varbvs)
install.packages("varbvs")For detailed discussion on the comparison methods, please see the package documentations and reference therein.
For each method, its implementation has a wrapper in code/method_wrapper.R.
For each simulation setting, its implementation has a wrapper in code/sim_wrapper.R.
We provide *.Rmd files for reproducing all the simulations in analysis folder.
We provide *.RDS files for storing all the results from analysis/*.Rmd in results folder.
We provide analysis/plots_for_the_paper.RDS for reproducing all the figures in our manuscript MR.ASH.
| Figure | Description | Source code for reproducing the result | Source code for plotting | |
|---|---|---|---|---|
Figure 1 |
Flexibility of MR.ASH prior, in terms of shrinkage operator and penalty | analysis/Figure1_Sourcecode |
analysis/Figure1_Plot |
|
Figure 2 and Figure 3 |
Adaptation to Sparsity (Penalized Linear Regression and Bayesian Linear Regression) | analysis/Figure2_Sourcecode1 analysis/Figure2_Sourcecode2 |
analysis/Figure2_Plot and analysis/Figure3_Plot |
|
Figure 4 |
Sample size, signal strength, signal shape | analysis/Figure4_Sourcecode1 analysis/Figure4_Sourcecode2 |
analysis/Figure4_Plot |
|
Figure 5 |
Computation time (scalability) | analysis/Figure5_Sourcecode |
analysis/Figure5_Plot |
|
Figure 6 |
Relative Performance (boxplot) | analysis/Figure6_Sourcecode |
analysis/Figure6_Plot |
|
Figure 7 |
Initialization and Update Order | analysis/Figure7_Sourcecode |
analysis/Figure7_Plot |
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Copyright (c) 2019-2020, Youngseok Kim.
All source code and software in this repository are made available under the terms of the MIT license. See file LICENSE for the full text of the license.
The mr.ash R package was developed by Youngseok Kim.