Experiments DB Dev
8a18c894dd
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>
78 lines
3.4 KiB
Plaintext
78 lines
3.4 KiB
Plaintext
==============================================================================
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PAPER LME REPLICATION -- mergeNaive (full range)
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
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behavior = successful reaches (COUNT per session)
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N = 11 rats, 170 sessions (day raw; day 0 = paper "Day 1")
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day coverage: stim(B2) 0..22, control(A2) 0..26
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** WARNING: unequal day coverage -- the full-range stim:day interaction
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extrapolates the control group's line and is CONFOUNDED here (the paper's
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groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
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==============================================================================
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FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 170
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Fixed effects coefficients 4
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Random effects coefficients 11
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Covariance parameters 2
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Formula:
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behavior ~ 1 + day*stim + (1 | rat)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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1526.4 1545.2 -757.2 1514.4
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 30.553 4.7932 6.3742 166 1.7469e-09
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{'day' } 3.5345 0.33399 10.583 166 2.5084e-20
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{'stim' } 10.155 8.0021 1.2691 166 0.2062
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{'day:stim' } 1.5835 0.58611 2.7018 166 0.0076138
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Lower Upper
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21.089 40.016
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2.8751 4.1939
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-5.6439 25.954
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0.42633 2.7407
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Random effects covariance parameters (95% CIs):
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Group: rat (11 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.2449
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Lower Upper
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5.388 15.863
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 19.866 17.802 22.168
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effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
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----------------------------------------------------------------------------
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stim x day (interaction) t(166)= 2.70 F(1)= 7.299 p=0.007614 p=0.007598 (df=170)
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day (learning) t(166)= 10.58 F(1)=111.990 p=2.508e-20 p=2.011e-20 (df=170)
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stim (main, Day 1) t(166)= 1.27 F(1)= 1.611 p=0.2062 p=0.217 (df=23)
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interaction 95% CI: [+0.43, +2.74]
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HONEST LME -- per-rat random slope (day|rat): interaction F(1,11.4)=0.92, p=0.3565
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Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
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slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
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random slope collapses the interaction DF toward the animal count -- the honest test.
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INTERPRETATION
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- stim x day interaction: SIGNIFICANT (p=0.0076, slope diff=+1.58) -> tDCS (Box-B2) improves FASTER -- benefit accumulates over training.
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- stim main effect on Day 1 (our day 0): n.s. (p=0.2062) -> groups are comparable on Day 1.
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
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F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
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