This repository contains the Stan files used in the hierarchical Bayesian linear model (HBLM.stan), the hierarchical Bayesian Gaussian Process model (HBGPM.stan) and the simple Bayesian linear model (NoSite.stan) as described in the paper "Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models" (Bayer et al., 2022).
Instructions on how to structure input files
int<lower=1> N; // total number of observations
vector[N] Y; // response variable (cortical thickness)
int<lower=1> K; // number of population-level effects
matrix[N, K] X; // population-level design matrix
Gaussian process parts population level effects
int<lower=1> Kgp_1;
int<lower=1> Dgp_1; // number of dimensions of GP (= one == Age)
vector[Dgp_1] Xgp_1[N]; // input vector of GP = Age vector again
Data for group-level effects of ID 1
int<lower=1> J_1[N]; // Site vector, lookup table for site
int<lower=1> N_1; // number of sites == 20
int<lower=1> M_1; // number of group level effects == 2 == slope and intercept for site
vector[N] Z_1_1; // long vector for intercept regressor of site == all ones
int prior_only; // should the likelihood be ignored?
Test input
int<lower=1> N_test; //number of test cases
matrix[N_test, K] X_test; // test cases design matrix
Data for group-level effects of ID 1 test cases
int<lower=1> J_1_test[N_test]; //site index
vector[N_test] Z_1_1_test; // test set: long vector for intercept regressor of site == all ones
vector[Dgp_1] Xgp_2[N_test]; // test set: input vector of GP = Age vector again
vector[N_test] Y_test;
Running the code via rstan or pystan
The stan code can be run via the R or Python interface libraries. For R, the libraries rstan and rstanarm can be downloaded via CRAN. Alterantively, the latest version of rstan can be downloaded from the rstan Github.
For Python, pystan can be installed via the pip install system.
The rstan Github also provides information on how to configure the C++ Toolchain on your machine and some useful information on the general use of rstan and stan.
Running the code in a singularity container
Install singularity on your machine. A guide for installation on different operating systems can be found here
A good example of how rstan can be run in singularity is via an image provided by Wytamma Wirth.
once singularity is installed, pull the following container:
singularity pull docker://jrnold/rstan
This should download the image 'rstan_latest.sif' to your computer (it might take a while).
In singularity, now run the image using the command
singularity shell rstan_latest.sif
to execute the image.
Run singularity by typing
singularity
in the command line.
The singularity image comes with a version of R that has the rstan library installed.
Typing R after the singularity prompt should load R:
Singularity> R
R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.