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experiments-database/analysis/matlab/results/lme_unmerged_d0_13.txt
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Experiments DB Dev 8a18c894dd feat(matlab): add Satterthwaite DF + honest random-slope test to LME reports
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>
2026-07-22 15:45:19 -04:00

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==============================================================================
LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_13
==============================================================================
model: success ~ day * tDCS + (1|subject) [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
N = 6 subjects, 70 sessions
day coverage: Box-B2 0..13, Box-A2 0..13
(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
main effect below is the group difference on Day 1 -- comparable to the paper.)
==============================================================================
FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 70
Fixed effects coefficients 4
Random effects coefficients 6
Covariance parameters 2
Formula:
success ~ 1 + day*tDCS + (1 | subject)
Model fit statistics:
AIC BIC LogLikelihood Deviance
586.72 600.22 -287.36 574.72
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 21.7 4.5484 4.7709 66 1.0537e-05
{'day' } 6.4863 0.71372 9.0881 66 3.0346e-13
{'tDCS' } 6.0226 6.3135 0.95392 66 0.3436
{'day:tDCS' } 1.5467 0.93755 1.6497 66 0.10376
Lower Upper
12.619 30.781
5.0614 7.9113
-6.5827 18.628
-0.32523 3.4185
Random effects covariance parameters (95% CIs):
Group: subject (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 14.676 12.436 17.32
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
------------------------------------------------------------------------------
days x tDCS (interaction) t(66)= 1.65 F(1)= 2.721 p=0.1038 p=0.1035 (df=70)
days (learning) t(66)= 9.09 F(1)= 82.593 p=3.035e-13 p=1.823e-13 (df=70)
tDCS (main, at Day 1) t(66)= 0.95 F(1)= 0.910 p=0.3436 p=0.3434 (df=70)
HONEST LME -- per-animal random slope (day|subject): interaction F(1,12.8)=1.64, p=0.2227
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=0.1038, slope diff=1.55) -> slopes are parallel (no differential change over training).
- days (learning): SIGNIFICANT (p=3e-13) -> performance improves with training.
- tDCS main effect on Day 1 (our day 0): n.s. (p=0.3436) -> the groups are comparable (as in the paper) on Day 1.
Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
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.
(Our N and exact statistics differ; this replicates the MODEL FORM on our data.)