README.md

In gigg: Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Grouping Structure

R package gigg

Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Grouping Structure

Overview

This package implements a Gibbs sampler corresponding to a Group Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation.

Installation

If the devtools package is not yet installed, install it first:

install.packages('devtools')

# install the package from Github:
devtools::install_github('umich-cphds/gigg') 

Once installed, load the package:

library(gigg)

Examples

GIGG regression Gibbs sampler with fixed hyperparameters:

X = concentrated$X
C = concentrated$C
Y = as.vector(concentrated$Y)
grp_idx = concentrated$grps
alpha_inits = concentrated$alpha
beta_inits = concentrated$beta

gf = gigg(X, C, Y, method = "fixed", grp_idx, alpha_inits, beta_inits,
          n_burn_in = 500, n_samples = 1000, n_thin = 1, 
          verbose = TRUE, btrick = FALSE, stable_solve = TRUE)

GIGG regression Gibbs sampler with hyperparameter estimation via Marginal Maximum Likelihood Estimation:

X = concentrated$X
C = concentrated$C
Y = as.vector(concentrated$Y)
grp_idx = concentrated$grps
alpha_inits = concentrated$alpha
beta_inits = concentrated$beta

gf_mmle = gigg(X, C, Y, method = "mmle", grp_idx, alpha_inits, beta_inits,
                n_burn_in = 500, n_samples = 1000, n_thin = 1, 
                verbose = TRUE, btrick = FALSE, stable_solve = TRUE)

Current Suggested Citation

Boss, J., Datta, J., Wang, X., Park, S.K., Kang, J., & Mukherjee, B. (2021). Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors. arXiv preprint arXiv:2102.10670.

gigg documentation built on March 9, 2021, 5:07 p.m.