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>
77 lines
3.2 KiB
Plaintext
77 lines
3.2 KiB
Plaintext
==============================================================================
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PAPER LME REPLICATION -- unmerged (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 = 6 rats, 71 sessions (day raw; day 0 = paper "Day 1")
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day coverage: stim(B2) 0..14, 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 71
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Fixed effects coefficients 4
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Random effects coefficients 6
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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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596.33 609.9 -292.16 584.33
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 21.53 5.5382 3.8874 67 0.00023506
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{'day' } 6.6587 0.72367 9.2014 67 1.6706e-13
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{'stim' } 8.0204 7.6977 1.0419 67 0.3012
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{'day:stim' } 0.93747 0.9187 1.0204 67 0.3112
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Lower Upper
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10.475 32.584
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5.2143 8.1032
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-7.3444 23.385
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-0.89627 2.7712
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Random effects covariance parameters (95% CIs):
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Group: rat (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.7931
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Lower Upper
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2.4723 13.574
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 14.163 11.93 16.813
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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(67)= 1.02 F(1)= 1.041 p=0.3112 p=0.3111 (df=70)
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day (learning) t(67)= 9.20 F(1)= 84.665 p=1.671e-13 p=1.04e-13 (df=71)
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stim (main, Day 1) t(67)= 1.04 F(1)= 1.086 p=0.3012 p=0.3116 (df=18)
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interaction 95% CI: [-0.90, +2.77]
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HONEST LME -- per-rat random slope (day|rat): interaction F(1,8.8)=0.61, p=0.456
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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: n.s. (p=0.3112, slope diff=+0.94) -> slopes parallel -- no differential learning rate over this window.
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- stim main effect on Day 1 (our day 0): n.s. (p=0.3012) -> 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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