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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
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analysis/matlab/tdcs_lme_report.m
+21 -3
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@@ -25,11 +25,12 @@ s = [s sprintf('\n')];
s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];
s = [s sprintf('%-27s %-14s %-13s %s\n', 'effect', 't (df)', 'F (df1)', 'p')];
s = [s sprintf('%s\n', repmat('-', 1, 70))];
s = [s sprintf('%-27s %-20s %-12s %s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)')];
s = [s sprintf('%s\n', repmat('-', 1, 78))];
s = [s localRow('days x tDCS (interaction)', L.interaction)];
s = [s localRow('days (learning)', L.day)];
s = [s localRow('tDCS (main, at Day 1)', L.tDCS)];
s = [s localHonest(L.interRS)];
s = [s sprintf('\nINTERPRETATION\n')];
if L.interaction.p >= 0.05
@@ -66,7 +67,24 @@ fprintf(fid, '%s', s);
end
function r = localRow(name, e)
r = sprintf('%-27s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', name, e.df, e.t, e.df1, e.F, e.p);
r = sprintf('%-27s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', ...
name, e.df, e.t, e.df1, e.F, e.p, e.pSatt, e.dfSatt);
end
function s = localHonest(rs)
%LOCALHONEST Report the random-slope interaction (the honest LME test) + why
% Satterthwaite on the random-intercept model above barely changes the DF.
if rs.ok
s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
'interaction F(%d,%.1f)=%.2f, p=%.4g\n'], rs.df1, rs.df2, rs.F, rs.p);
else
s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
'model did not converge for this window.\n']);
end
s = [s sprintf([' Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual\n' ...
' (the slope''s error is at the session level), so it does NOT fix pseudoreplication.\n' ...
' Letting each animal have its OWN slope collapses the interaction DF toward the animal\n' ...
' count -- this, and the per-animal slope test, are the honest learning-rate inference.\n'])];
end
function t = localSig(p)