analysis(matlab): per-variation subfolders (grouping x window) for the paper LME
Add make_variations.m + variation_analyze.m generating 20 self-contained subfolders under analysis/matlab/variations/, one per grouping x day-window, each with exactly three files: data.csv (curated subset), analyze.m (a simple standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv. Groupings (stim=1 / stim=0, other groups dropped): unmerge B2 vs A2 right_only B2+Right vs A2 (Box-A dropped) naive_a2 B2 vs A2+Naive (Right, Box-A dropped) naive_boxa B2 vs A2+Naive+Box-A (Right dropped) Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Experiments DB Dev
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% Variation analysis -- the paper's linear mixed model on the successful-reach
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% COUNT, fit on this folder's curated data subset.
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%
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% model: behavior ~ stim + day + stim:day + (1|rat)
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% behavior = successful reaches (count per session)
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% stim = 1 for the treatment group(s), 0 for the control group(s)
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% day = training day within this window (0 = first analyzed day)
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% rat = subject (random intercept)
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%
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% Self-contained: reads data.csv beside this script and writes result.txt.
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% Run headless from this folder with: matlab -batch "analyze"
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% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
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C = m.Coefficients; A = anova(m); ci = coefCI(m);
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gi = @(t) find(strcmp(C.Name, t), 1);
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ga = @(t) find(strcmp(A.Term, t), 1);
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row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
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C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
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maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
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if abs(maxT - maxC) > 2
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cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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else
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cov = '(equal day coverage over this window)';
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end
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ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
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if pI >= 0.05
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verdict = 'n.s. -- slopes parallel (no differential learning rate)';
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elseif eI > 0
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verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
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else
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verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
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end
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bar = repmat('=', 1, 78);
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raw = regexprep(evalc('disp(m)'), '</?strong>', '');
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s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
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s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
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s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
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s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
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s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
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numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
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s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
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s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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s = [s row('stim x day (interaction)', 'day:stim')];
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s = [s row('day (learning)', 'day')];
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s = [s row('stim (main, window start)', 'stim')];
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s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
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s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
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s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
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fprintf('%s', s);
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fid = fopen(fullfile(here, 'result.txt'), 'w');
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fprintf(fid, '%s', s);
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fclose(fid);
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% Machine-readable handoff for the summary table (see make_variations.m).
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VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
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'nObs', height(D), 'interP', pI, 'interEst', eI, ...
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'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
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'covEqual', abs(maxT - maxC) <= 2);
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subject,group,day,success,total,stim
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Banh-mi-1,Electrode-Box-B2,0,13,84,1
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Banh-mi-1,Electrode-Box-B2,1,35,86,1
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Banh-mi-1,Electrode-Box-B2,2,47,110,1
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Banh-mi-1,Electrode-Box-B2,3,65,140,1
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Banh-mi-1,Electrode-Box-B2,4,70,127,1
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Banh-mi-1,Electrode-Box-B2,5,102,142,1
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Banh-mi-2,Electrode-Box-A2,0,25,97,0
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Banh-mi-2,Electrode-Box-A2,1,22,101,0
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Banh-mi-2,Electrode-Box-A2,2,22,119,0
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Banh-mi-2,Electrode-Box-A2,3,29,118,0
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Banh-mi-2,Electrode-Box-A2,4,27,136,0
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Banh-mi-2,Electrode-Box-A2,5,43,146,0
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Egg-tart-1,Electrode-Box-B2,0,7,56,1
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Egg-tart-1,Electrode-Box-B2,1,16,78,1
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Egg-tart-1,Electrode-Box-B2,2,23,103,1
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Egg-tart-1,Electrode-Box-B2,3,63,120,1
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Egg-tart-1,Electrode-Box-B2,4,69,132,1
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Egg-tart-1,Electrode-Box-B2,5,83,136,1
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Egg-tart-2,Electrode-Box-A2,0,9,32,0
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Egg-tart-2,Electrode-Box-A2,1,2,38,0
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Egg-tart-2,Electrode-Box-A2,2,31,93,0
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Egg-tart-2,Electrode-Box-A2,3,44,101,0
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Egg-tart-2,Electrode-Box-A2,4,54,131,0
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Egg-tart-2,Electrode-Box-A2,5,84,139,0
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Khoai-lang-1,Right-Electrode,0,3,69,1
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Khoai-lang-1,Right-Electrode,1,18,97,1
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Khoai-lang-1,Right-Electrode,2,27,84,1
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Khoai-lang-1,Right-Electrode,3,46,121,1
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Khoai-lang-1,Right-Electrode,4,65,143,1
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Khoai-lang-1,Right-Electrode,5,76,141,1
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Root-beer-1,Electrode-Box-B2,0,11,85,1
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Root-beer-1,Electrode-Box-B2,1,18,76,1
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Root-beer-1,Electrode-Box-B2,2,40,105,1
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Root-beer-1,Electrode-Box-B2,3,55,134,1
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Root-beer-1,Electrode-Box-B2,4,75,136,1
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Root-beer-1,Electrode-Box-B2,5,64,133,1
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Root-beer-2,Electrode-Box-A2,0,22,74,0
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Root-beer-2,Electrode-Box-A2,1,31,87,0
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Root-beer-2,Electrode-Box-A2,2,49,134,0
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Root-beer-2,Electrode-Box-A2,3,31,89,0
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Root-beer-2,Electrode-Box-A2,4,60,140,0
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Root-beer-2,Electrode-Box-A2,5,84,147,0
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==============================================================================
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VARIATION: right_only_d0_5
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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day = training day within window (0 = first analyzed day)
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treatment (stim=1): Electrode-Box-B2, Right-Electrode
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control (stim=0): Electrode-Box-A2
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N = 7 rats, 42 sessions raw day coverage: treat 0..5, control 0..5
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(equal day coverage over this window)
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==============================================================================
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FULL MODEL SUMMARY -- fitlme
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 42
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Fixed effects coefficients 4
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Random effects coefficients 7
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Covariance parameters 2
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Formula:
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behavior ~ 1 + day*stim + (1 | rat)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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331.16 341.59 -159.58 319.16
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 12.524 5.1493 2.4321 38 0.019831
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{'day' } 9.8571 1.3751 7.1681 38 1.4583e-08
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{'stim' } -4.9762 6.8119 -0.73051 38 0.46956
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{'day:stim' } 5.3071 1.8191 2.9174 38 0.0058973
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Lower Upper
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2.0996 22.948
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7.0733 12.641
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-18.766 8.8138
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1.6245 8.9898
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Random effects covariance parameters (95% CIs):
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Group: rat (7 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.2482
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Lower Upper
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2.2427 12.282
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 9.9638 7.8829 12.594
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(38)= 2.92 F(1)= 8.511 p=0.005897
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day (learning) t(38)= 7.17 F(1)= 51.382 p=1.458e-08
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stim (main, window start) t(38)= -0.73 F(1)= 0.534 p=0.4696
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interaction 95% CI: [+1.62, +8.99]
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INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.31)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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