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-B2,6,90,131,1
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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-lang-1,Right-Electrode,6,79,146,1
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Khoai-lang-1,Right-Electrode,7,81,153,1
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Khoai-lang-1,Right-Electrode,8,98,144,1
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Khoai-lang-1,Right-Electrode,9,101,149,1
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Khoai-lang-1,Right-Electrode,10,103,150,1
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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: right_only_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, Right-Electrode
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control (stim=0): 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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230.64 239.05 -109.32 218.64
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 74.2 6.0309 12.303 26 2.4061e-12
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{'day' } 2.4 1.8291 1.3121 26 0.20096
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{'stim' } 11.9 7.3864 1.6111 26 0.11924
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{'day:stim' } 2.425 2.2402 1.0825 26 0.28898
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Lower Upper
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61.803 86.597
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-1.3599 6.1599
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-3.2829 27.083
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-2.1799 7.0299
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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'} 5.7092
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Lower Upper
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2.5487 12.789
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 8.1802 6.1646 10.855
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(26)= 1.08 F(1)= 1.172 p=0.289
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day (learning) t(26)= 1.31 F(1)= 1.722 p=0.201
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stim (main, window start) t(26)= 1.61 F(1)= 2.596 p=0.1192
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interaction 95% CI: [-2.18, +7.03]
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.289, slope diff=+2.42)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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