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experiments-database/analysis/matlab/results/lme_mergeA2_d0_10.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_mergeA2_d0_10
==============================================================================
model: success ~ day * tDCS + (1|subject) [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
N = 8 subjects, 83 sessions
day coverage: Box-B2 0..10, Box-A2 0..10
(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 83
Fixed effects coefficients 4
Random effects coefficients 8
Covariance parameters 2
Formula:
success ~ 1 + day*tDCS + (1 | subject)
Model fit statistics:
AIC BIC LogLikelihood Deviance
673.84 688.35 -330.92 661.84
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 15.599 3.7298 4.1822 79 7.4136e-05
{'day' } 8.3258 0.66962 12.434 79 2.7592e-20
{'tDCS' } 4.1737 5.2381 0.79679 79 0.42796
{'day:tDCS' } 1.3833 0.9137 1.514 79 0.13402
Lower Upper
8.175 23.023
6.9929 9.6586
-6.2525 14.6
-0.43535 3.202
Random effects covariance parameters (95% CIs):
Group: subject (8 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 13.04 11.2 15.183
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).
- days (learning): SIGNIFICANT (p=2.8e-20) -> performance improves with training.
- tDCS main effect on Day 1 (our day 0): n.s. (p=0.4280) -> 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.)