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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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==============================================================================
VARIATION: right_only_d0_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 7 rats, 84 sessions raw day coverage: treat 0..13, control 0..13
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 84
Fixed effects coefficients 4
Random effects coefficients 7
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
695.18 709.77 -341.59 683.18
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 21.7 4.3761 4.9587 80 3.9065e-06
{'day' } 6.4863 0.68669 9.4459 80 1.1708e-14
{'stim' } 3.5986 5.6847 0.63303 80 0.52852
{'day:stim' } 1.7002 0.84679 2.0078 80 0.048042
Lower Upper
12.991 30.409
5.1198 7.8529
-7.7143 14.911
0.015009 3.3853
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 3.1353e-15
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 14.12 12.139 16.425
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(80)= 2.01 F(1)= 4.031 p=0.04804 p=0.04788 (df=84)
day (learning) t(80)= 9.45 F(1)= 89.224 p=1.171e-14 p=7.518e-15 (df=84)
stim (main, window start) t(80)= 0.63 F(1)= 0.401 p=0.5285 p=0.5284 (df=84)
interaction 95% CI: [+0.02, +3.39]
HONEST LME (per-animal random slope, day|rat): interaction F(1,20.2)=2.92, p=0.1029
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
model above is the honest learning-rate test -- DF collapses toward the animal count.)
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.04804, slope diff=+1.70)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.