Experiments DB Dev
8a18c894dd
Every fitlme-based report (lme_*, paper_*, phase_*, and the variations' analyze.m) now shows, per effect: residual-DF p, Satterthwaite-DF p, and -- for the interaction -- an HONEST test from a per-animal random-SLOPE model (day|rat), whose Satterthwaite DF collapses toward the animal count. New: tdcs_random_slope_interaction.m (shared helper). Wired into tdcs_lme, tdcs_paper_lme, tdcs_phase_lme, variation_analyze; SUMMARY.csv gains interaction_p_satt / interaction_p_rs. Regenerated all results/, variations/, matched-effort outputs. Key point this surfaces: Satterthwaite ~= residual on the random-INTERCEPT model (the slope's error is at session level), so it does NOT fix pseudoreplication; the random-slope model does. Effect: full-range mergeA2 interaction 0.009 -> 0.75 (collapses); unmerge_d0_5 0.015 -> 0.13 (n.s.); the pooled-control early windows survive honestly (naive_a2_d0_5 0.001 -> 0.028; naive_boxa_d0_5 0.003 -> 0.036). Suite 42/42. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
97 lines
4.6 KiB
Matlab
97 lines
4.6 KiB
Matlab
function tdcs_lme_report(L, scenario, cfg)
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%TDCS_LME_REPORT Print + save the days x tDCS linear-mixed-model report.
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% TDCS_LME_REPORT(L, SCENARIO, CFG) formats the result of TDCS_LME to the
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% console and to analysis/matlab/results/<SCENARIO>.txt, in the style of the
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% published result (interaction, days, tDCS effects) plus interpretation and
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% the paper's reference numbers.
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bar = repmat('=', 1, 78);
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s = sprintf('%s\n', bar);
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s = [s sprintf('LINEAR MIXED MODEL (days x tDCS) -- scenario: %s\n', scenario)];
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s = [s sprintf('%s\n', bar)];
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s = [s sprintf('model: success ~ day * tDCS + (1|subject) [tDCS: %s = 1 vs %s = 0]\n', ...
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cfg.anchorHigh, cfg.anchorLow)];
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s = [s sprintf('N = %d subjects, %d sessions\n', L.nSubjects, L.nObs)];
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s = [s sprintf('day coverage: Box-B2 0..%d, Box-A2 0..%d\n', L.maxDayB, L.maxDayA)];
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s = [s sprintf('(day is raw and 0-indexed: our day 0 = the paper''s "Day 1", so the tDCS\n')];
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s = [s sprintf(' main effect below is the group difference on Day 1 -- comparable to the paper.)\n')];
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if abs(L.maxDayB - L.maxDayA) > 2
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s = [s sprintf(['** WARNING: the groups'' day coverage is UNEQUAL (differ by %d days). The\n' ...
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' interaction/slope over this window EXTRAPOLATES the shorter group''s line and is\n' ...
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' CONFOUNDED -- prefer the _d0_%d window (both groups have data throughout). **\n'], ...
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abs(L.maxDayB - L.maxDayA), min(L.maxDayB, L.maxDayA))];
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end
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s = [s sprintf('\n')];
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s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];
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s = [s sprintf('%-27s %-20s %-12s %s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)')];
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s = [s sprintf('%s\n', repmat('-', 1, 78))];
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s = [s localRow('days x tDCS (interaction)', L.interaction)];
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s = [s localRow('days (learning)', L.day)];
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s = [s localRow('tDCS (main, at Day 1)', L.tDCS)];
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s = [s localHonest(L.interRS)];
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s = [s sprintf('\nINTERPRETATION\n')];
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if L.interaction.p >= 0.05
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interTxt = 'slopes are parallel (no differential change over training)';
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elseif L.interaction.estimate > 0
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interTxt = 'the tDCS (Box-B2) group improves FASTER -- benefit ACCUMULATES over training';
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else
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interTxt = 'the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows)';
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end
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s = [s sprintf(' - days x tDCS interaction: %s (p=%.4f, slope diff=%.2f) -> %s.\n', ...
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localSig(L.interaction.p), L.interaction.p, L.interaction.estimate, interTxt)];
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s = [s sprintf(' - days (learning): %s (p=%.2g) -> performance improves with training.\n', ...
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localSig(L.day.p), L.day.p)];
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s = [s sprintf(' - tDCS main effect on Day 1 (our day 0): %s (p=%.4f) -> the groups %s on Day 1.\n', ...
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localSig(L.tDCS.p), L.tDCS.p, ...
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localPick(L.tDCS.p < 0.05, 'already DIFFER', 'are comparable (as in the paper)'))];
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s = [s sprintf('\nPaper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;\n')];
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s = [s sprintf(' days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.\n')];
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s = [s sprintf(' (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)\n')];
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fprintf('%s', s);
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thisDir = fileparts(mfilename('fullpath'));
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resDir = fullfile(thisDir, 'results');
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if ~exist(resDir, 'dir'); mkdir(resDir); end
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fid = fopen(fullfile(resDir, [scenario '.txt']), 'w');
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if fid < 0
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error('tdcs_lme_report:fopen', 'Cannot open results file for "%s".', scenario);
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end
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cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
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fprintf(fid, '%s', s);
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end
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function r = localRow(name, e)
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r = sprintf('%-27s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', ...
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name, e.df, e.t, e.df1, e.F, e.p, e.pSatt, e.dfSatt);
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end
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function s = localHonest(rs)
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%LOCALHONEST Report the random-slope interaction (the honest LME test) + why
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% Satterthwaite on the random-intercept model above barely changes the DF.
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if rs.ok
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s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
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'interaction F(%d,%.1f)=%.2f, p=%.4g\n'], rs.df1, rs.df2, rs.F, rs.p);
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else
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s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
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'model did not converge for this window.\n']);
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end
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s = [s sprintf([' Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual\n' ...
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' (the slope''s error is at the session level), so it does NOT fix pseudoreplication.\n' ...
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' Letting each animal have its OWN slope collapses the interaction DF toward the animal\n' ...
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' count -- this, and the per-animal slope test, are the honest learning-rate inference.\n'])];
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end
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function t = localSig(p)
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if p < 0.05; t = 'SIGNIFICANT'; else; t = 'n.s.'; end
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end
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function t = localPick(b, yes, no)
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if b; t = yes; else; t = no; end
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end
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