#########################################################
#                                                       #
# Script for running the Poisson Log-Normal model with  #
# an inverse-gamma shrinkage prior                      #
#                                                       #
# By: Viktor Jonsson,   2016-10-15                      #
#                                                       #
#########################################################


#Initialize reuqired packages
library("rjags")
library("coda")


#Source required functions

source("GLMMfunctions.R")




# Data preparations, note data should be supplied in a n x m matrix
# containing two groups of samples


exampleData <- quickDataSimulation()


Y <- exampleData

group=c(rep(0,3),rep(1,3)) # indicator vector indicating which samples
                           # belong to which group. Note this implementation
                           # the two groups to be in consecutive order



nR <- dim(Y)[1]
nC <- dim(Y)[2]


normalizingConstants <- colSums(Y) #For simplicity total sum scaling is used


#MCMC run settings
nrAdapt=1000
nrBurnIn=1000
nrSamples=20000
nrThin = 1

#runs the mcmc for the selected settings and returns the significance score
#for each feature
result <- GLMMLoanInvGamma(Y=Y,group = group, N = normalizingConstants,
                           nrAdapt=nrAdapt,nrBurnIn=nrBurnIn,nrSamples=nrSamples,
                           nrThin=nrThin)