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feat(matlab): add Satterthwaite DF + honest random-slope test to LME reports

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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>
This commit is contained in:
Experiments DB Dev
2026-07-22 15:45:19 -04:00
parent 5fcd97f81a
commit 8a18c894dd
77 changed files with 2066 additions and 345 deletions
Download Patch File Download Diff File Expand all files Collapse all files
analysis/matlab/tdcs_phase_lme.m
+15 -7
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@@ -30,8 +30,8 @@ s = sprintf('%s\n', bar);
s = [s sprintf('PHASED days x tDCS LME -- %s (Box-B2 vs Box-A2)\n', mergeKey)];
s = [s sprintf('%s\n', bar)];
s = [s sprintf('model per phase: success ~ dayp*tDCS + (1|subject) [dayp = day - phaseStart]\n\n')];
s = [s sprintf('%-9s N A2slope B2slope day p tDCS(lvl) p interaction p slopeDiff [95%% CI]\n', 'phase')];
s = [s sprintf('%s\n', repmat('-', 1, 92))];
s = [s sprintf('%-8s N A2slp B2slp day p tDCS p intP(res) intP(Satt) intP(RS) slopeDiff [95%% CI]\n', 'phase')];
s = [s sprintf('%s\n', repmat('-', 1, 104))];
rawSummaries = '';
for k = 1:numel(phases)
@@ -42,6 +42,9 @@ for k = 1:numel(phases)
lme = fitlme(Tp, 'success ~ dayp*tDCS + (1|subject)');
C = lme.Coefficients; A = anova(lme); ci = coefCI(lme);
As = anova(lme, 'DFMethod', 'satterthwaite');
rs = tdcs_random_slope_interaction(Tp, ...
'success ~ dayp*tDCS + (dayp|subject)', 'dayp:tDCS');
ii = strcmp(C.Name, 'dayp:tDCS');
di = strcmp(C.Name, 'dayp');
@@ -53,22 +56,27 @@ for k = 1:numel(phases)
e.dayP = A.pValue(strcmp(A.Term, 'dayp'));
e.tDCSlevelP = A.pValue(strcmp(A.Term, 'tDCS'));
e.interP = A.pValue(strcmp(A.Term, 'dayp:tDCS'));
e.interPsatt = As.pValue(strcmp(As.Term, 'dayp:tDCS')); % Satterthwaite DF
e.interPrs = rs.p; e.rsOk = rs.ok; % honest random-slope
e.interEst = C.Estimate(ii);
e.interCI = ci(ii, :);
P.phases{k} = e;
s = [s sprintf('%d-%-6d %d %6.2f %6.2f %-9.2g %-11.3f %-13.3f %+.2f [%+.2f, %+.2f]\n', ...
if rs.ok; rsStr = sprintf('%.3f', rs.p); else; rsStr = 'n/a'; end
s = [s sprintf('%d-%-5d %d %5.2f %5.2f %-8.2g %-8.3f %-9.3f %-10.3f %-9s %+.2f [%+.2f,%+.2f]\n', ...
ph(1), ph(2), e.nSub, e.a2Slope, e.b2Slope, e.dayP, e.tDCSlevelP, ...
e.interP, e.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
e.interP, e.interPsatt, rsStr, e.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
rawSummaries = [rawSummaries tdcs_model_summary(lme, ...
sprintf('phase %d-%d: success ~ dayp*tDCS + (1|subject)', ph(1), ph(2))) ...
sprintf('\n')]; %#ok<AGROW>
end
s = [s sprintf(['\nNote: the interaction p (and CI) use fitlme observation-level DF and are\n' ...
'ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early\n' ...
'phase carries the Box-B2 faster-acquisition signal; late phases converge.\n'])];
s = [s sprintf(['\nColumns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite\n' ...
'DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope\n' ...
'(dayp|subject) model, the honest test (DF collapses toward the animal count; ''n/a'' if it\n' ...
'did not converge). The early phase carries the Box-B2 faster-acquisition signal; late\n' ...
'phases converge.\n'])];
s = [s rawSummaries];