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Matlab Toolbox for Bayesian Estimation (MBE)

Synopsis

This is a Matlab Toolbox for Bayesian Estimation. The basis of the code is a Matlab implementation of Kruschke's R code described in the following paper (Kruschke, 2013), book (Kruschke, 2014) and website (http://www.indiana.edu/~kruschke/BEST/). This toolbox is intended to provide the user with similiar possible analyses as Kruschke's code does, yet makes it applicable in a Matlab-only environment. In addition, I will try to add additional features in the future to make it applicable for more than just a group comparison.

Code Example

This example uses the data provided in Kruschke's BEST paper (2013). Run the script mbe_2gr_example.m.

%% Load some data
% EXAMPLE DATA (see Kruschke, 2013)
% see http://www.indiana.edu/~kruschke/BEST/ for R code

y1 = [101,100,102,104,102,97,105,105,98,101,100,123,105,103,100,95,102,106,...
    109,102,82,102,100,102,102,101,102,102,103,103,97,97,103,101,97,104,...
    96,103,124,101,101,100,101,101,104,100,101];
y2 = [99,101,100,101,102,100,97,101,104,101,102,102,100,105,88,101,100,...
    104,100,100,100,101,102,103,97,101,101,100,101,99,101,100,100,...
    101,100,99,101,100,102,99,100,99];

y = [y1,y2]; % combine data into one vector
x = [ones(1,length(y1)),2*ones(1,length(y2))]; % create group membership code
nTotal = length(y);


%% Specify prior constants, shape and rate for gamma distribution
% See Kruschke (2011) for further description
mu1PriorMean = mean(y);
mu1PriorSD = std(y)*5;    % flat prior
mu2PriorMean = mean(y);
mu2PriorSD = std(y)*5;
sigma1PriorMode = std(y);
sigma1PriorSD = std(y)*5;
sigma2PriorMode = std(y);
sigma2PriorSD = std(y)*5;
nuPriorMean = 30;
nuPriorSD = 30;

% Now get shape and rate for gamma distribution
[Sh1, Ra1] = mbe_gammaShRa(sigma1PriorMode,sigma1PriorSD,'mode');
[Sh2, Ra2] = mbe_gammaShRa(sigma2PriorMode,sigma2PriorSD,'mode');
[ShNu, RaNu] = mbe_gammaShRa(nuPriorMean,nuPriorSD,'mean');

% Save prior constants in a structure for later use with matjags
dataList = struct('y',y,'x',x,'nTotal',nTotal,...
    'mu1PriorMean',mu1PriorMean,'mu1PriorSD',mu1PriorSD,...
    'mu2PriorMean',mu2PriorMean,'mu2PriorSD',mu2PriorSD,...
    'Sh1',Sh1,'Ra1',Ra1,'Sh2',Sh2,'Ra2',Ra2,'ShNu',ShNu,'RaNu',RaNu);

Now specify the MCMC properties and run JAGS:

%% Specify MCMC properties
% Number of MCMC steps that are saved for EACH chain
% This is different to Rjags, where you would define the number of
% steps to be saved for all chains together (in this example 12000)
numSavedSteps = 4000;

% Number of separate MCMC chains
nChains = 3;

% Number of steps that are thinned, matjags will only keep every nth
% step. This does not affect the number of saved steps. I.e. in order
% to compute 10000 saved steps, matjags/JAGS will compute 50000 steps
% If memory isn't an issue, Kruschke recommends to use longer chains
% and no thinning at all.
thinSteps = 5;

% Number of burn-in samples
burnInSteps = 1000;

% The parameters that are to be monitored
parameters = {'mu','sigma','nu'};

%% Initialize the chain
% Initial values of MCMC chains based on data:
mu = [mean(y1),mean(y2)];
sigma = [std(y1),std(y2)];
% Regarding initial values: (1) sigma will tend to be too big if
% the data have outliers, and (2) nu starts at 5 as a moderate value. These
% initial values keep the burn-in period moderate.

% Set initial values for latent variable in each chain
for i=1:nChains
    initsList(i) = struct('mu', mu, 'sigma',sigma,'nu',5);
end

%% Specify the JAGS model
% This will write a JAGS model to a text file
% You can also write the JAGS model directly to a text file or use
% one of the models that come with this toolbox

modelString = [' model {\n',...
    '    for ( i in 1:nTotal ) {\n',...
    '    y[i] ~ dt( mu[x[i]] , 1/sigma[x[i]]^2 , nu )\n',...
    '    }\n',...
    '    mu[1] ~ dnorm( mu1PriorMean , 1/mu1PriorSD^2 )  # prior for mu[1]\n',...
    '    sigma[1] ~ dgamma( Sh1 , Ra1 )     # prior for sigma[1]\n',...
    '    mu[2] ~ dnorm( mu2PriorMean , 1/mu2PriorSD^2 )  # prior for mu[2]\n',...
    '    sigma[2] ~ dgamma( Sh2 , Ra2 )     # prior for sigma[2]\n',...
    '    nu ~ dgamma( ShNu , RaNu ) # prior for nu \n'...
    '}'];
fileID = fopen('mbe_2gr_example.txt','wt');
fprintf(fileID,modelString);
fclose(fileID);
model = fullfile(pwd,'mbe_2gr_example.txt');

%% Run the chains using matjags and JAGS
% In case you have the Parallel Computing Toolbox, use ('doParallel',1)
[samples, stats, mcmcChain] = matjags(...
    dataList,...
    model,...
    initsList,...
    'monitorparams', parameters,...
    'nChains', nChains,...
    'nBurnin', burnInSteps,...
    'thin', thinSteps,...
    'verbosity',2,...
    'nSamples',numSavedSteps);
%% Restructure the output
% This transforms the output of matjags into the format that mbe is
% using
mcmcChain = mbe_restructChains(mcmcChain);

Examine the chains with the mbe_diagMCMC() function:

mbe_diagMCMC(mcmcChain);

You will get these figures: alt tag

Now examine the posterior distributions of the parameters.

%% Examine the results
% At this point, we want to use all the chains at once, so we
% need to concatenate the individual chains to one long chain first
mcmcChain = mbe_concChains(mcmcChain);
% Get summary and posterior plots
summary = mbe_2gr_summary(mcmcChain);
% Data has to be in a cell array
data{1} = y1;
data{2} = y2;
mbe_2gr_plots(data,mcmcChain);

These are examples of the figures that can be created to show the posterior distributions: alt tag alt tag alt tag

Functions

Examples

  • mbe_2gr_example.m
  • This is an example script for a comparison of two groups.
  • mbe_2gr_plots.m
  • Makes histogram of data with superimposed posterior prediction check and plots posterior distribution of monitored parameters.
  • mbe_2gr_summary.m
  • Computes summary statistics for all parameters of a 2 group comparison.This will only work for a mcmc chain with parameters mu1,mu2,sigma1,sigma2 and nu.
  • mbe_1gr_example.m
  • This is an example script for a one group Bayes estimation.
  • mbe_1gr_plots.m
  • Makes histogram of data with superimposed posterior prediction check and plots posterior distribution of monitored parameters.
  • mbe_1gr_summary.m
  • Computes summary statistics for all parameters.This will only work for a mcmc chain with parameters mu1,sigma1 and nu.

MCMC Diagnostics

  • mbe_diagMCMC.m
  • Plots autocorrelation, parameter trace, shrink factor and parameter density.
  • mbe_tracePlot.m
  • Creates a trace plot for a parameter of a MCMC chain.
  • mbe_acfPlot.m
  • Plots autocorrelation of MCMC chain parameter for every chain.
  • mbe_gelmanPlot.m
  • This plot shows the evolution of Gelman and Rubin's shrink factor as the number of iterations increases. More than one chain is needed.
  • mbe_mcmcDensPlot.m
  • Plots probability density function MCMC chains of one parameter.Also shows the HDI of the parameter for every chain.

Posterior Plots

  • mbe_plotPost.m
  • Plotting posterior distribution with highest density interval.
  • mbe_plotPairs.m
  • Plots matrix of scatter plots for any combination of parameters.
  • mbe_plotData.m
  • Plots histogram of observed data. If specified, adds superimposed posterior predictive check. Works only when comparing two groups.

Utilities

  • mbe_restructChains.m

Restructures MCMC output (of matjags). Matjags creates multiple structures when more than one chain is used, but stores parameters with the same name in the same variable. This function splits up the parameters and creates one structure with all the monitored parameters. Parameters are stored as matrix with NxMxT, where N is the number of iterations per chain, M is the number of chains and T is the number of time steps (only for time course analysis).

  • mbe_concChains.m

Concatenates several MCMC chains into one.

  • mbe_summary.m

Computes summary statistics for one parameter of mcmc chain Summary statistics include mean, median, mode, HDI and if a comparison value is specified the percentage of parameter data points above the threshold. This is useful i.e. for comparing groups and calculating the difference. The comparison value would then be 0.

  • mbe_gammaShRa.m

Calculates shape and rate for gamma distribution.

  • mbe_hdi.m

Computes highest density interval from a sample of representative values, estimated as shortest credible interval.

Installation

The MBE toolbox uses the open source software JAGS (Just Another Gibbs Sampler) to conduct Markov-Chain-Monte-Carlo sampling. Instead of using Rjags (as you would when using Kruschke's code), MBE uses the Matlab-JAGS interface matjags.m that will communicate with JAGS and import the results back to Matlab.

  • JAGS can be downloaded here: http://mcmc-jags.sourceforge.net/

  • matjags.m can be downloaded here: http://psiexp.ss.uci.edu/research/programs_data/jags/

  • Further installation descriptions are provided on these websites. If you have installed JAGS and matjags.m successfully, the MBE Toolbox should work right out of the box. Just add the directory to your Matlab path.

  • The MBE Toolbox uses additional functions obtained via Matlab's File Exchange. The functions are contained in this toolbox and don't need to be downloaded separately. The licenses of these functions are stored in the corresponding folder.

References

Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573.

Kruschke, J. K. (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and STAN (2nd ed.). Amsterdam: Academic Press.

License

Copyright Nils Winter, 2016