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| Original file line number | Diff line number | Diff line change |
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| function allresults = correlations_regression_lags | ||
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| global mypath datasets datasetnames | ||
| addpath(genpath('~/code/Tools')); | ||
| warning off; close all; | ||
| cols = cbrewer('qual', 'Paired', 10); | ||
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| numlags = 6; | ||
| vars = {'z_correct', 'z_error', 'v_correct', 'v_error', 'repeat_correct', 'repeat_error'}; | ||
| cnt = 1; | ||
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| for d = 1:length(datasets), | ||
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| dat = readtable(sprintf('%s/summary/%s/allindividualresults.csv', mypath, datasets{d})); | ||
| dat = dat(dat.session == 0, :); | ||
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| for m = 1:length(vars), | ||
| alldata.(vars{m}) = nan(numlags, size(dat, 1)); | ||
| end | ||
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| % ALL MODELS THAT WERE RAN | ||
| 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'}; | ||
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| % ============================= % | ||
| % 1. DETERMINE THE BEST MODEL | ||
| % ============================= % | ||
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| 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.aic; | ||
| catch | ||
| fprintf('%s/%s/%s/model_comparison.csv NOT FOUND\n', ... | ||
| mypath, datasets{d}, mdls{m}) | ||
| end | ||
| end | ||
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| % everything relative to the full model | ||
| mdldic = bsxfun(@minus, mdldic, mdldic(1)); | ||
| mdldic = mdldic(2:end); | ||
| mdls = mdls(2:end); | ||
| [~, bestMdl] = min(mdldic); | ||
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| % now take the hybrid model for this best-fitting lag | ||
| bestmodelname = sprintf('regressdczlag%s', mdls{bestMdl}(end)); | ||
| disp(bestmodelname); | ||
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| % ========================================================== % | ||
| % 2. FOR THE BEST-FITTING MODEL, GET HISTORY WEIGHTS | ||
| % ========================================================== % | ||
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| % ignore lag 1 - just take the average of lag 2:bestmodel | ||
| for l = 2:str2double(bestmodelname(end)), | ||
| lname = num2str(l); | ||
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| % get regression weights | ||
| for v = 1:length(vars), | ||
| switch vars{v} | ||
| case 'z_correct' | ||
| alldata.(vars{v})(l,:) = ... | ||
| (dat.(['z_prev' lname 'resp__' bestmodelname]) + ... | ||
| dat.(['z_prev' lname 'stim__' bestmodelname])); | ||
| case 'z_error' | ||
| alldata.z_error(l,:) = ... | ||
| (dat.(['z_prev' lname 'resp__' bestmodelname]) - ... | ||
| dat.(['z_prev' lname 'stim__' bestmodelname])); | ||
| case 'v_correct' | ||
| alldata.v_correct(l,:) = ... | ||
| (dat.(['v_prev' lname 'resp__' bestmodelname]) + ... | ||
| dat.(['v_prev' lname 'stim__' bestmodelname])); | ||
| case 'v_error' | ||
| alldata.v_error(l,:) = ... | ||
| (dat.(['v_prev' lname 'resp__' bestmodelname]) - ... | ||
| dat.(['v_prev' lname 'stim__' bestmodelname])); | ||
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| case 'repeat_error' | ||
| alldata.(vars{v})(l,:) = dat.(['repetition_error' num2str(l)])... | ||
| - arrayfun(@trivial_probabilities, dat.repetition_error1, repmat(l, size(dat, 1), 1)); | ||
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| case 'repeat_correct' | ||
| alldata.(vars{v})(l,:) = dat.(['repetition_correct' num2str(l)]) ... | ||
| - arrayfun(@trivial_probabilities, dat.repetition_correct1, repmat(l, size(dat, 1), 1)); | ||
| end | ||
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| end | ||
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| end | ||
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| % assign to structure - correct choices | ||
| allresults(1).z_prevresp = nanmean(alldata.z_correct); | ||
| allresults(1).v_prevresp = nanmean(alldata.v_correct); | ||
| allresults(1).criterionshift = nanmean(alldata.repeat_correct); | ||
| alltitles{1} = datasetnames{d}; | ||
| allresults(1).marker = 'o'; | ||
| allresults(1).meancolor = [0 0 0]; | ||
| allresults(1).scattercolor = [0.5 0.5 0.5]; | ||
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| % also after error choices | ||
| allresults(2).z_prevresp = nanmean(alldata.z_error); | ||
| allresults(2).v_prevresp = nanmean(alldata.v_error); | ||
| allresults(2).criterionshift = nanmean(alldata.repeat_error); | ||
| alltitles{2} = datasetnames{d}; | ||
| allresults(2).marker = 's'; | ||
| allresults(2).meancolor = cols(6, :); | ||
| allresults(2).scattercolor = cols(5, :); | ||
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| % ========================================================== % | ||
| % COMPUTE CORRELATIONS | ||
| % ========================================================== % | ||
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| for a = 1:length(allresults), | ||
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| % SAVE CORRELATIONS FOR OVERVIEW PLOT | ||
| % COMPUTE THE SPEARMANS CORRELATION AND ITS CONFIDENCE INTERVAL! | ||
| [alldat(cnt).corrz, alldat(cnt).corrz_ci, alldat(cnt).pz, alldat(cnt).bfz] = ... | ||
| spearmans(allresults(a).z_prevresp(:), allresults(a).criterionshift(:)); | ||
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| [alldat(cnt).corrv, alldat(cnt).corrv_ci, alldat(cnt).pv, alldat(cnt).bfv] = ... | ||
| spearmans(allresults(a).v_prevresp(:), allresults(a).criterionshift(:)); | ||
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| alldat(cnt).datasets = datasets{d}; | ||
| alldat(cnt).datasetnames = alltitles{a}; | ||
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| % also add the difference in correlation, steigers test | ||
| [r,p,rlo,rup] = spearmans(allresults(a).v_prevresp(:), allresults(a).z_prevresp(:)); | ||
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| [rhodiff, rhodiffci, pval] = rddiffci(alldat(cnt).corrz, alldat(cnt).corrv, ... | ||
| r, numel(allresults(a).v_prevresp), 0.05); | ||
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| alldat(cnt).corrdiff = rhodiff; | ||
| alldat(cnt).corrdiff_ci = rhodiffci; | ||
| alldat(cnt).pdiff = pval; | ||
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| % plotting layout for forestPlot | ||
| alldat(cnt).marker = allresults(a).marker; | ||
| alldat(cnt).scattercolor = allresults(a).scattercolor; | ||
| alldat(cnt).meancolor = allresults(a).meancolor; | ||
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| cnt = cnt + 1; | ||
| end | ||
| end | ||
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| % ========================================================== % | ||
| % COMPUTE CORRELATIONS | ||
| % ========================================================== % | ||
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| forestPlot(alldat(1:2:end)); | ||
| print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/forestplot_regressionHDDM_prevcorrect.pdf')); | ||
| forestPlot(alldat(2:2:end)); | ||
| print(gcf, '-dpdf', sprintf('~/Data/serialHDDM/forestplot_regressionHDDM_preverror.pdf')); | ||
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| end | ||
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| function [ vec_repeat ] = trivial_probabilities(p,lag) | ||
| vec_repeat(1)=p; | ||
| for i=2:lag; | ||
| vec_repeat(i)=p*vec_repeat(i-1)+(1-p)*(1-vec_repeat(i-1)); | ||
| end | ||
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| vec_repeat = vec_repeat(end); | ||
| end | ||
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