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>
69 lines
2.8 KiB
Plaintext
69 lines
2.8 KiB
Plaintext
==============================================================================
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VARIATION: unmerge_d6_10
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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day = training day within window (0 = first analyzed day)
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treatment (stim=1): Electrode-Box-B2
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control (stim=0): Electrode-Box-A2
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N = 5 rats, 25 sessions raw day coverage: treat 6..10, control 6..10
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(equal day coverage over this window)
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==============================================================================
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FULL MODEL SUMMARY -- fitlme
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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 25
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Fixed effects coefficients 4
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Random effects coefficients 5
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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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196.97 204.29 -92.486 184.97
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 74.2 6.4017 11.591 21 1.3774e-10
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{'day' } 2.4 1.9295 1.2439 21 0.22726
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{'stim' } 14.333 8.2646 1.7343 21 0.097522
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{'day:stim' } 1.7667 2.491 0.70923 21 0.48598
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Lower Upper
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60.887 87.513
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-1.6126 6.4126
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-2.8539 31.521
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-3.4136 6.9469
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Random effects covariance parameters (95% CIs):
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Group: rat (5 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 6.1065
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Lower Upper
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2.5428 14.665
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 8.6289 6.3295 11.764
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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(21)= 0.71 F(1)= 0.503 p=0.486 p=0.4864 (df=20)
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day (learning) t(21)= 1.24 F(1)= 1.547 p=0.2273 p=0.2279 (df=20)
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stim (main, window start) t(21)= 1.73 F(1)= 3.008 p=0.09752 p=0.1098 (df=11)
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interaction 95% CI: [-3.41, +6.95]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,5.0)=0.40, p=0.5549
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(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
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model above is the honest learning-rate test -- DF collapses toward the animal count.)
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.486, slope diff=+1.77)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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