Experiments DB Dev
963fd889b2
Make tdcs_paper_lme window-aware (windowKey 'full'|'d0_10'|'d0_13', default 'full' -> unchanged filename). Windowed runs give the two anchor arms equal day coverage, so the stim:day interaction is not confounded by the full-range coverage imbalance; the report now states whether coverage is equal and adds an INTERPRETATION block. Add switch cases paper_mergeNaive_d0_10 / _d0_13 and include them in run_all. Finding: unlike mergeA2 (fair-window interaction n.s.), the pooled-naive control keeps the interaction significant in the fair d0_13 window (F(1)=7.07, p=0.0088, +1.80 [+0.46,+3.14]; equal on Day 1 p=0.20), reproducing the paper without the coverage confound; d0_10 is borderline (p=0.051). Tests: tLme asserts equal coverage / no warning / one full summary for both windows and a significant positive d0_13 interaction; bad windowKey errors. Suite 41/41. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
72 lines
2.8 KiB
Plaintext
72 lines
2.8 KiB
Plaintext
==============================================================================
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PAPER LME REPLICATION -- mergeNaive (days 0-13)
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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, 138 sessions (day raw; day 0 = paper "Day 1")
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day coverage: stim(B2) 0..13, control(A2) 0..13
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(Equal day coverage -- the stim:day interaction over this window is NOT
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confounded by the full-range coverage imbalance.)
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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 138
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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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1164.9 1182.4 -576.44 1152.9
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 16.254 4.2865 3.792 134 0.00022519
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{'day' } 6.4041 0.42677 15.006 134 1.7662e-30
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{'stim' } 8.9845 7.0391 1.2764 134 0.20403
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{'day:stim' } 1.7992 0.67672 2.6587 134 0.008801
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Lower Upper
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7.7764 24.732
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5.5601 7.2482
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-4.9377 22.907
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0.46073 3.1376
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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'} 8.1481
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Lower Upper
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4.7633 13.938
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 14.825 13.109 16.766
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(134)= 2.66 F(1)= 7.068 p=0.008801
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day (learning) t(134)= 15.01 F(1)= 225.182 p=1.766e-30
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stim (main, Day 1) t(134)= 1.28 F(1)= 1.629 p=0.204
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interaction 95% CI: [+0.46, +3.14]
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INTERPRETATION
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- stim x day interaction: SIGNIFICANT (p=0.0088, slope diff=+1.80) -> 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=0.2040) -> 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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