barplots_modelcomparison_regression.m

function barplots_modelcomparison_regression()

% Code to fit the history-dependent drift diffusion models as described in
% Urai AE, de Gee JW, Tsetsos K, Donner TH (2019) Choice history biases subsequent evidence accumulation. eLife, in press.
%
% MIT License
% Copyright (c) Anne Urai, 2019
% anne.urai@gmail.com

addpath(genpath('~/code/Tools'));
warning off; close all;
global datasets datasetnames mypath colors

modelIC = {'aic'};
for s = 1:length(modelIC),
    
    % ============================================ %
    % DIC COMPARISON BETWEEN DC, Z AND BOTH
    % ============================================ %
    
    % 1. STIMCODING, only prevresps
    mdls = {'regress_nohist', ...
        'regress_z_lag1', ...
        'regress_dc_lag1', ...
        'regress_dcz_lag1', ...
        'regress_z_lag2', ...
        'regress_dc_lag2', ...
        'regress_dcz_lag2', ...
        'regress_z_lag3', ...
        'regress_dc_lag3', ...
        'regress_dcz_lag3', ...
        'regress_z_lag4', ...
        'regress_dc_lag4', ...
        'regress_dcz_lag4', ...
        'regress_z_lag5', ...
        'regress_dc_lag5', ...
        'regress_dcz_lag5', ...
        'regress_z_lag6', ...
        'regress_dc_lag6', ...
        'regress_dcz_lag6'};

    numlags = 6;
    lagnames = {'1', '2', '3', '4', '5', '6'};

    for d = 1:length(datasets),
        close all;
        subplot(4, 4, 1);
        getPlotModelIC(mdls, modelIC{s}, d, colors);
        title(datasetnames{d});
        set(gca, 'xtick', 1:numlags, 'xticklabel', lagnames);
        xlabel('Lags (# trials)')
        ylabel({'\DeltaAIC from model'; 'without history'}, 'interpreter', 'tex');

        drawnow; tightfig;
        print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/modelcomparison_regression_%s_d%d.pdf', modelIC{s}, d));
        disp(d);
    end
end

close all;

end

% ============================================ %
% DIC COMPARISON BETWEEN DC, Z AND BOTH
% ============================================ %

function getPlotModelIC(mdls, s, d, colors)

global datasets mypath 
axis square; hold on;
plot([1 6], [0 0], 'color', 'k', 'linewidth', 0.5);

mdldic = nan(1, length(mdls));
for m = 1:length(mdls),
    try
    modelcomp = readtable(sprintf('%s/%s/%s/model_comparison.csv', ...
        mypath, datasets{d}, mdls{m}), 'readrownames', true);
    mdldic(m) = modelcomp.(s);
catch
    fprintf('%s/%s/%s/model_comparison.csv  NOT FOUND\n', ...
        mypath, datasets{d}, mdls{m})
    end
end

% everything relative to the full model
mdldic = bsxfun(@minus, mdldic, mdldic(1));
mdldic = mdldic(2:end);
mdls = mdls(2:end);
[~, bestMdl] = min(mdldic);

for c = 1:3,
    b = plot(1:6, mdldic(c:3:end), 'o-', 'color', colors(c, :),'markersize', 5,  ...
        'markerfacecolor', colors(c, :), 'markeredgecolor', [1 1 1]);
    
end

axis square; axis tight; 
set(gca, 'xtick', 1:6);
offsetAxes; box off;
% set(gca, 'color', 'none');
set(gca, 'xcolor', 'k', 'ycolor', 'k');

end