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: prev_f_full
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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): b2_f
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control (stim=0): a2_f
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N = 24 rats, 231 sessions raw day coverage: treat 0..9, control 0..9
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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 231
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Fixed effects coefficients 4
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Random effects coefficients 24
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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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1416.2 1436.9 -702.11 1404.2
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 7.7376 1.4647 5.2828 227 2.9782e-07
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{'day' } 1.3487 0.15094 8.9352 227 1.4298e-16
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{'stim' } 1.8961 2.0698 0.9161 227 0.36059
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{'day:stim' } 0.56023 0.21043 2.6623 227 0.0083151
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Lower Upper
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4.8515 10.624
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1.0513 1.6461
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-2.1823 5.9745
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0.14559 0.97488
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Random effects covariance parameters (95% CIs):
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Group: rat (24 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 4.3164
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Lower Upper
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3.1535 5.9081
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 4.4892 4.0771 4.9429
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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(227)= 2.66 F(1)= 7.088 p=0.008315 p=0.008365 (df=208)
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day (learning) t(227)= 8.94 F(1)= 79.838 p=1.43e-16 p=2.16e-16 (df=209)
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stim (main, window start) t(227)= 0.92 F(1)= 0.839 p=0.3606 p=0.3656 (df=37)
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interaction 95% CI: [+0.15, +0.97]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,24.0)=3.01, p=0.09541
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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 SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.56)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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