analysis(matlab): Box-A pooling variations (b2 vs a2+a, b2+a vs a2)
Add make_boxa_variations.m producing 6 variation folders (data.csv + analyze.m + result.txt) for the paper LME over windows 0-5, 6-10, 0-10: boxa_a2 B2 vs A2 + Box-A (b2 vs a2+a) boxa_b2 B2 + Box-A vs A2 (b2+a vs a2) plus variations/boxa_summary.csv (residual / Satterthwaite / random-slope interaction p). Early window (0-5) is obs-level significant (res p~0.03-0.04) but n.s. under the honest random-slope test (rs p~0.13); later windows n.s. 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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As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
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% Honest test: refit with a per-animal random SLOPE so the interaction DF
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% collapses toward the animal count (guarded -- may not converge in short windows).
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rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
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wst = warning('off', 'all');
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try
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mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
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Ar = anova(mr, 'DFMethod', 'satterthwaite');
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ri = strcmp(Ar.Term, 'day:stim');
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rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
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catch
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end
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warning(wst);
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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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gs = @(t) find(strcmp(As.Term, t), 1);
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row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\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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As.pValue(gs(t)), As.DF2(gs(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 %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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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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if rsOk
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s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
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else
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s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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end
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s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
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' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
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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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'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
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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-B2,6,90,131,1
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Banh-mi-1,Electrode-Box-B2,7,109,148,1
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Banh-mi-1,Electrode-Box-B2,8,104,137,1
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Banh-mi-1,Electrode-Box-B2,9,119,150,1
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Banh-mi-1,Electrode-Box-B2,10,121,158,1
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Banh-mi-2,Electrode-Box-A2,6,74,146,0
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Banh-mi-2,Electrode-Box-A2,7,70,148,0
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Banh-mi-2,Electrode-Box-A2,8,65,130,0
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Banh-mi-2,Electrode-Box-A2,9,79,151,0
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Banh-mi-2,Electrode-Box-A2,10,93,152,0
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Egg-tart-1,Electrode-Box-B2,6,71,142,1
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Egg-tart-1,Electrode-Box-B2,7,79,138,1
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Egg-tart-1,Electrode-Box-B2,8,98,142,1
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Egg-tart-1,Electrode-Box-B2,9,89,139,1
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Egg-tart-1,Electrode-Box-B2,10,96,143,1
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Egg-tart-2,Electrode-Box-A2,6,85,145,0
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Egg-tart-2,Electrode-Box-A2,7,79,143,0
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Egg-tart-2,Electrode-Box-A2,8,76,131,0
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Egg-tart-2,Electrode-Box-A2,9,88,149,0
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Egg-tart-2,Electrode-Box-A2,10,81,151,0
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Khoai-tay-1,Electrode-Box-A,6,81,137,0
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Khoai-tay-1,Electrode-Box-A,7,78,147,0
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Khoai-tay-1,Electrode-Box-A,8,91,132,0
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Khoai-tay-1,Electrode-Box-A,9,99,146,0
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Khoai-tay-1,Electrode-Box-A,10,94,143,0
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Root-beer-1,Electrode-Box-B2,6,104,139,1
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Root-beer-1,Electrode-Box-B2,7,98,148,1
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Root-beer-1,Electrode-Box-B2,8,81,145,1
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Root-beer-1,Electrode-Box-B2,9,89,156,1
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Root-beer-1,Electrode-Box-B2,10,105,158,1
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==============================================================================
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VARIATION: boxa_a2_d6_10
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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
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control (stim=0): Electrode-Box-A, Electrode-Box-A2
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N = 6 rats, 30 sessions raw day coverage: treat 6..10, control 6..10
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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 30
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Fixed effects coefficients 4
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Random effects coefficients 6
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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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231.74 240.15 -109.87 219.74
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 75.867 5.1856 14.63 26 4.6141e-14
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{'day' } 3.1667 1.4985 2.1132 26 0.044334
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{'stim' } 12.667 7.3335 1.7272 26 0.095989
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{'day:stim' } 1 2.1192 0.47188 26 0.64095
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Lower Upper
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65.208 86.526
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0.086483 6.2469
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-2.4075 27.741
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-3.356 5.356
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Random effects covariance parameters (95% CIs):
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Group: rat (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 6.3444
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Lower Upper
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2.9639 13.581
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 8.2076 6.1852 10.891
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effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
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----------------------------------------------------------------------------
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stim x day (interaction) t(26)= 0.47 F(1)= 0.223 p=0.6409 p=0.6413 (df=24)
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day (learning) t(26)= 2.11 F(1)= 4.466 p=0.04433 p=0.04517 (df=24)
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stim (main, window start) t(26)= 1.73 F(1)= 2.983 p=0.09599 p=0.1083 (df=13)
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interaction 95% CI: [-3.36, +5.36]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.18, p=0.6872
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(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
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model above is the honest learning-rate test -- DF collapses toward the animal count.)
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.6409, slope diff=+1.00)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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