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_mergeA2_d0_13
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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 = 8 subjects, 98 sessions
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day coverage: Box-B2 0..13, Box-A2 0..13
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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 98
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Fixed effects coefficients 4
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Random effects coefficients 8
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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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809.08 824.59 -398.54 797.08
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 19.458 3.7166 5.2352 94 1.0011e-06
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{'day' } 7.1842 0.54693 13.135 94 5.4028e-23
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{'tDCS' } 5.841 5.1946 1.1244 94 0.26369
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{'day:tDCS' } 1.0024 0.73807 1.3581 94 0.17769
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Lower Upper
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12.078 26.837
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6.0982 8.2701
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-4.473 16.155
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-0.4631 2.4678
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Random effects covariance parameters (95% CIs):
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Group: subject (8 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 0
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Lower Upper
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NaN NaN
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 14.123 12.278 16.245
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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(94)= 1.36 F(1)= 1.844 p=0.1777 p=0.1776 (df=98)
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days (learning) t(94)= 13.14 F(1)=172.537 p=5.403e-23 p=2.463e-23 (df=98)
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tDCS (main, at Day 1) t(94)= 1.12 F(1)= 1.264 p=0.2637 p=0.2636 (df=98)
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HONEST LME -- per-animal random slope (day|subject): interaction F(1,16.2)=1.37, p=0.2593
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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=0.1777, slope diff=1.00) -> slopes are parallel (no differential change over training).
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- days (learning): SIGNIFICANT (p=5.4e-23) -> performance improves with training.
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- tDCS main effect on Day 1 (our day 0): n.s. (p=0.2637) -> 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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