## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,fig.width=6, fig.height=4
)

## ----simulation, eval=FALSE---------------------------------------------------
# # Running the simulation with specific parameters
# a <- rgm:::sim.rgm(p=27, B=5, n=146, mcmc_iter = 100, seed=1234)

## ----run-experiment, eval=FALSE-----------------------------------------------
# 
# # Fitting the model
# res <- rgm:::rgm(a$data, X=a$X, iter=10000)
# 

## ----load, echo=FALSE, eval=FALSE---------------------------------------------
# load("SimRes_p87.RData")
# #res = smaller_res

## ----plot-sample-theta, echo=FALSE, warning=FALSE, eval=FALSE-----------------
# # Loading the RGM package
# suppressPackageStartupMessages(library(rgm))
# 
# suppressPackageStartupMessages(library(ggplot2))
# 
# suppressPackageStartupMessages(library(pROC))
# 
# suppressPackageStartupMessages(library(gplots))
# 
# suppressPackageStartupMessages(library(grid))
# 
# suppressPackageStartupMessages(library(gridExtra))
# 
# suppressPackageStartupMessages(library(dendextend))
# 
# 

## ----eval=FALSE---------------------------------------------------------------
# 
# ps = rgm:::post_processing_rgm(simulated_data = a,results = res)
# 
# 

## ----beta_convergence, warning=FALSE, eval=FALSE------------------------------
# ps$beta_convergence

## ----rgm_recovery, eval=FALSE-------------------------------------------------
# 
# ps$rgm_recovery

## ----roc_plot, eval=FALSE-----------------------------------------------------
# ps$roc_plot

## ----estimation_of_alpha, eval=FALSE------------------------------------------
# ps$estimation_of_alpha

## ----posterior_distribution, eval=FALSE---------------------------------------
# ps$posterior_distribution

## ----edge_prob, eval=FALSE----------------------------------------------------
# ps$edge_prob