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: naive_boxa_d0_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
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control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
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N = 11 rats, 138 sessions raw day coverage: treat 0..13, control 0..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 138
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Fixed effects coefficients 4
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Random effects coefficients 11
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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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1172.3 1189.8 -580.14 1160.3
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 15.925 4.1997 3.792 134 0.00022515
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{'day' } 6.7124 0.401 16.739 134 1.2129e-34
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{'stim' } 11.848 7.9908 1.4827 134 0.1405
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{'day:stim' } 1.3064 0.75238 1.7363 134 0.084806
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Lower Upper
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7.619 24.231
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5.9193 7.5055
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-3.9564 27.652
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-0.1817 2.7945
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Random effects covariance parameters (95% CIs):
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Group: rat (11 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 8.76
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Lower Upper
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5.1941 14.774
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 15.18 13.424 17.167
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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(134)= 1.74 F(1)= 3.015 p=0.08481 p=0.08489 (df=129)
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day (learning) t(134)= 16.74 F(1)=280.201 p=1.213e-34 p=2.161e-34 (df=131)
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stim (main, window start) t(134)= 1.48 F(1)= 2.198 p=0.1405 p=0.1519 (df=23)
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interaction 95% CI: [-0.18, +2.79]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,25.3)=2.32, p=0.1403
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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=0.08481, slope diff=+1.31)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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