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
2.9 KiB
Plaintext
69 lines
2.9 KiB
Plaintext
==============================================================================
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VARIATION: right_only_d6_13
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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, Right-Electrode
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control (stim=0): Electrode-Box-A2
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N = 6 rats, 42 sessions raw day coverage: treat 6..13, control 6..13
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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 42
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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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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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309.98 320.4 -148.99 297.98
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 75.163 5.7598 13.049 38 1.2935e-15
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{'day' } 1.7153 1.01 1.6984 38 0.097608
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{'stim' } 11.525 7.0338 1.6386 38 0.10956
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{'day:stim' } 2.7579 1.1891 2.3193 38 0.02585
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Lower Upper
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63.503 86.823
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-0.32925 3.7599
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-2.7138 25.764
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0.35071 5.1651
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Random effects covariance parameters (95% CIs):
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Group: rat (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 6.4688
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Lower Upper
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3.3423 12.52
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.3669 5.8525 9.2731
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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(38)= 2.32 F(1)= 5.379 p=0.02585 p=0.02591 (df=38)
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day (learning) t(38)= 1.70 F(1)= 2.885 p=0.09761 p=0.09766 (df=38)
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stim (main, window start) t(38)= 1.64 F(1)= 2.685 p=0.1096 p=0.1304 (df=11)
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interaction 95% CI: [+0.35, +5.17]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,21.2)=4.96, p=0.03697
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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=0.02585, slope diff=+2.76)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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