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_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-A2, Naive
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N = 9 rats, 65 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 65
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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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535.5 548.54 -261.75 523.5
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 66.813 5.1278 13.03 61 2.7998e-19
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{'day' } 2.8262 0.83395 3.3889 61 0.0012345
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{'stim' } 21.913 8.8928 2.4641 61 0.016568
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{'day:stim' } 1.1553 1.4855 0.77769 61 0.43976
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Lower Upper
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56.56 77.067
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1.1586 4.4937
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4.1309 39.695
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-1.8152 4.1258
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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'} 9.7589
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Lower Upper
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5.6191 16.948
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 12.033 10.004 14.473
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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(61)= 0.78 F(1)= 0.605 p=0.4398 p=0.4399 (df=58)
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day (learning) t(61)= 3.39 F(1)= 11.485 p=0.001235 p=0.001274 (df=57)
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stim (main, window start) t(61)= 2.46 F(1)= 6.072 p=0.01657 p=0.02473 (df=17)
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interaction 95% CI: [-1.82, +4.13]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,7.7)=0.36, p=0.5635
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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=0.4398, slope diff=+1.16)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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