BayesCOOP

The repository contains the analysis codes from the Bayesian coopertive project.

Dependencies

BayesCOOP requires the following R package: devtools (for installation only). Please install it before installing BayesCOOP, which can be done as follows (execute from within a fresh R session):

install.packages("devtools")
library(devtools)

Installation

Once the dependencies are installed, BayesCOOP can be loaded using the following command:

devtools::install_github("himelmallick/BayesCOOP")
library(BayesCOOP)

Load libraries

library(BhGLM)
library(tidyverse)
library(caret)

Sourcing required functions

source("~/bayesCOOP/BC/bayesCoop.R")

Loading the StelzerDOS real dataset

data_train = get(load(url("https://raw.githubusercontent.com/himelmallick/IntegratedLearner/master/data/StelzerDOS.RData"))); 
rm(pcl)

data_test = get(load(url("https://raw.githubusercontent.com/himelmallick/IntegratedLearner/master/data/StelzerDOS_valid.RData"))); 
rm(pcl)

Pre-processing the longitudinal data by considering only baseline observations

Remove metabolomics from the train set to match with validation

data_train$feature_metadata = data_train$feature_metadata %>% dplyr::filter(featureType!='Metabolomics')
data_train$feature_table = data_train$feature_table[rownames(data_train$feature_metadata),]

Consider only baseline observations for the train set

positions = grep("A", colnames(data_train$feature_table), ignore.case = TRUE)
data_train$feature_table = data_train$feature_table[, positions]
data_train$sample_metadata = data_train$sample_metadata[positions, ]
rm(positions)

Consider only baseline observations for the validation set

positions = grep("G1", colnames(data_test$feature_table))
data_test$feature_table = data_test$feature_table[, positions]
data_test$sample_metadata = data_test$sample_metadata[positions, ]
rm(positions)

BayesCoop Implementation

set.seed(1)
result = bayesCoop(data_train, data_test, family = "gaussian", 
                   ss = c(0.05, 1), group = TRUE,
                   alpha_dirich = 1, maxit = 1, 
                   bbiters = 110, bbburn = 10,
                   abd_thresh = 0, prev_thresh = 0.1,
                   Warning = TRUE, verbose = TRUE)

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#result$beta_postmed

result$rho_postmed

## [1] 0.0001039843

#result$beta_samples

result$y_pred

## [1]  20.352509  -2.883056 -12.267622   5.012925  -4.011487 -13.139735  -6.224429
## [8]  13.160894

result$mspe

## [1] 488.5006

result$time

## [1] 3.172

Visualization of Posterior Samples

Let's check MCMC convergence of the reciprocal Bayesian LASSO estimator through two visualizations: trace plots and histograms.

######################################
# Visualization of Posterior Samples #
######################################

##############
# Trace Plot #
##############

library(coda)
par(mar=c(2,2,2,2))
plot(mcmc(result$beta_samples[,1:9]),density=FALSE,smooth=TRUE)

#############
# Histogram #
#############

library(psych)
multi.hist(result$beta_samples[,1:9],density=TRUE,main="")