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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/results/lme_mergeA2_d0_10.txt
+11 -5
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@@ -54,11 +54,17 @@ Group: Error
{'Res Std'} 13.04 11.2 15.183
effect t (df) F (df1) p
----------------------------------------------------------------------
days x tDCS (interaction) t(79)= 1.51 F(1)= 2.292 p=0.134
days (learning) t(79)= 12.43 F(1)= 154.593 p=2.759e-20
tDCS (main, at Day 1) t(79)= 0.80 F(1)= 0.635 p=0.428
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
------------------------------------------------------------------------------
days x tDCS (interaction) t(79)= 1.51 F(1)= 2.292 p=0.134 p=0.1338 (df=83)
days (learning) t(79)= 12.43 F(1)=154.593 p=2.759e-20 p=1.192e-20 (df=83)
tDCS (main, at Day 1) t(79)= 0.80 F(1)= 0.635 p=0.428 p=0.4278 (df=83)
HONEST LME -- per-animal random slope (day|subject): interaction F(1,20.3)=1.35, p=0.258
Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
(the slope's error is at the session level), so it does NOT fix pseudoreplication.
Letting each animal have its OWN slope collapses the interaction DF toward the animal
count -- this, and the per-animal slope test, are the honest learning-rate inference.
INTERPRETATION
- days x tDCS interaction: n.s. (p=0.1340, slope diff=1.38) -> slopes are parallel (no differential change over training).