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_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, Naive
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N = 9 rats, 45 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 45
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Fixed effects coefficients 4
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Random effects coefficients 9
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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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368.51 379.35 -178.25 356.51
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 62.1 5.5902 11.109 41 6.1328e-14
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{'day' } 5.9667 1.3623 4.3798 41 8.0273e-05
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{'stim' } 26.433 9.6824 2.73 41 0.0092904
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{'day:stim' } -1.8 2.3596 -0.76285 41 0.44992
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Lower Upper
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50.81 73.39
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3.2154 8.7179
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6.8793 45.987
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-6.5653 2.9653
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Random effects covariance parameters (95% CIs):
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Group: rat (9 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 10.986
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Lower Upper
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6.3454 19.02
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.552 8.376 13.294
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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(41)= -0.76 F(1)= 0.582 p=0.4499 p=0.4505 (df=36)
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day (learning) t(41)= 4.38 F(1)= 19.183 p=8.027e-05 p=9.813e-05 (df=36)
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stim (main, window start) t(41)= 2.73 F(1)= 7.453 p=0.00929 p=0.01541 (df=15)
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interaction 95% CI: [-6.57, +2.97]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=0.44, p=0.5223
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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=0.4499, slope diff=-1.80)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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