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: naive_a2_d0_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, Naive
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N = 10 rats, 104 sessions raw day coverage: treat 0..10, control 0..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 104
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Fixed effects coefficients 4
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Random effects coefficients 10
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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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869.17 885.04 -428.58 857.17
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 10.33 4.4952 2.298 100 0.023646
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{'day' } 8.0905 0.53615 15.09 100 1.598e-27
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{'stim' } 12.019 8.117 1.4807 100 0.14184
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{'day:stim' } 1.4337 0.92893 1.5434 100 0.12589
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Lower Upper
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1.4115 19.248
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7.0268 9.1542
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-4.0853 28.123
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-0.40926 3.2767
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Random effects covariance parameters (95% CIs):
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Group: rat (10 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 8.753
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Lower Upper
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5.0208 15.26
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 13.78 11.942 15.902
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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(100)= 1.54 F(1)= 2.382 p=0.1259 p=0.1261 (df=95)
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day (learning) t(100)= 15.09 F(1)=227.710 p=1.598e-27 p=4.059e-27 (df=96)
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stim (main, window start) t(100)= 1.48 F(1)= 2.192 p=0.1418 p=0.1543 (df=20)
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interaction 95% CI: [-0.41, +3.28]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=1.16, p=0.3093
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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=0.1259, slope diff=+1.43)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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