The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

ScaleSpikeSlab

Niloy Biswas

31/01/2022

ScaleSpikeSlab (S^3)

This package contains algorithms for Scalable Spike-and-Slab (S^3), a scalable Gibbs sampling implementation for high-dimensional Bayesian regression with the continuous spike-and-slab prior.

It is based on the article “Scalable Spike-and-Slab”, by Niloy Biswas, Lester Mackey and Xiao-Li Meng. The folder inst contains scripts to reproduce the results of the article.

Installation

The package can be installed from R via:

# install.packages("devtools")
devtools::install_github("niloyb/ScaleSpikeSlab/R_package")

# Install dependencies Rcpp, RcppEigen
install.packages(c("Rcpp", "RcppEigen"))
# Install additional packages to help with parallel computation and plotting
install.packages(c("doParallel", "doRNG", "foreach", "dplyr", "tidyr", 
                   "ggplot2", "latex2exp", "reshape2", "ggpubr"))

A tutorial with Riboflavin GWAS data

Import data and select hyperparameters.

set.seed(1)
library(ScaleSpikeSlab)

# Riboflavin linear regression dataset of Buhlmann et al. (2014) 
data(riboflavin)
X <- riboflavin$x
Xt <- t(X)
y <- riboflavin$y

# Choose hyperparamters
params <- spike_slab_params(n=nrow(X),p=ncol(X))

Run MCMC with S^3

library(doParallel)
registerDoParallel(cores = detectCores()-1)
library(foreach)

no_chains <- 50
sss_chain_z_output <- 
  foreach(i = c(1:no_chains), .combine=rbind)%dopar%{
  sss_chain <- spike_slab_linear(chain_length=5e3,burnin=1e3,X=X,Xt=Xt,y=y,
                               tau0=params$tau0,tau1=params$tau1,q=params$q,
                               verbose=FALSE,store=FALSE)
  return(as.vector(sss_chain$z_ergodic_avg))
}

Plot Spike-and-Slab marginal posterior probabilities for variable selection

library(dplyr)
library(ggplot2)
library(latex2exp)

riboflavin_df <- 
  data.frame(post_prob_mean=apply(sss_chain_z_output,2,mean),
             post_prob_sd=apply(sss_chain_z_output,2,sd),
             cov_index=c(1:ncol(X)), no_chains=no_chains) %>%
  arrange(desc(post_prob_mean)) %>%
  mutate(xaxis =1:n())


ggplot(riboflavin_df, aes(x=xaxis, y=post_prob_mean)) + 
  geom_point(size=2) + 
  geom_errorbar(aes(ymax=(post_prob_mean+3*post_prob_sd/sqrt(no_chains)), 
                    ymin=(post_prob_mean-3*post_prob_sd/sqrt(no_chains))),
                position=position_dodge(.9)) +
  xlab('Riboflavin Covariates') + 
  ylab(TeX('Marginal posterior probabilities')) +
  scale_x_continuous(trans='log10') + theme_classic(base_size = 12)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.