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
3.4 KiB
Plaintext
77 lines
3.4 KiB
Plaintext
==============================================================================
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LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_10
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==============================================================================
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model: success ~ day * tDCS + (1|subject) [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
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N = 6 subjects, 61 sessions
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day coverage: Box-B2 0..10, Box-A2 0..10
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(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
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main effect below is the group difference on Day 1 -- comparable to the paper.)
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==============================================================================
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FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
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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 61
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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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success ~ 1 + day*tDCS + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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499.59 512.25 -243.79 487.59
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 17.372 5.1906 3.3469 57 0.0014518
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{'day' } 7.9666 0.79053 10.077 57 2.8317e-14
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{'tDCS' } 4.9763 7.2862 0.68298 57 0.49739
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{'day:tDCS' } 1.5577 1.0483 1.4858 57 0.14283
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Lower Upper
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6.9783 27.766
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6.3836 9.5496
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-9.614 19.567
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-0.5416 3.6569
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Random effects covariance parameters (95% CIs):
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Group: subject (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.3536
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Lower Upper
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2.1542 13.305
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 12.508 10.37 15.086
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effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
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------------------------------------------------------------------------------
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days x tDCS (interaction) t(57)= 1.49 F(1)= 2.208 p=0.1428 p=0.1428 (df=57)
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days (learning) t(57)= 10.08 F(1)=101.556 p=2.832e-14 p=1.931e-14 (df=59)
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tDCS (main, at Day 1) t(57)= 0.68 F(1)= 0.466 p=0.4974 p=0.5037 (df=17)
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HONEST LME -- per-animal random slope (day|subject): interaction F(1,13.0)=1.46, p=0.2479
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Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
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(the slope's error is at the session level), so it does NOT fix pseudoreplication.
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Letting each animal have its OWN slope collapses the interaction DF toward the animal
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count -- this, and the per-animal slope test, are the honest learning-rate inference.
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INTERPRETATION
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- days x tDCS interaction: n.s. (p=0.1428, slope diff=1.56) -> slopes are parallel (no differential change over training).
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- days (learning): SIGNIFICANT (p=2.8e-14) -> performance improves with training.
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- tDCS main effect on Day 1 (our day 0): n.s. (p=0.4974) -> the groups are comparable (as in the paper) on Day 1.
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Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
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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.
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(Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
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