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>
158 lines
5.7 KiB
Plaintext
158 lines
5.7 KiB
Plaintext
==============================================================================
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PHASED days x tDCS LME -- mergeNaive (Box-B2 vs Box-A2)
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==============================================================================
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model per phase: success ~ dayp*tDCS + (1|subject) [dayp = day - phaseStart]
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phase N A2slp B2slp day p tDCS p intP(res) intP(Satt) intP(RS) slopeDiff [95% CI]
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--------------------------------------------------------------------------------------------------------
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0-5 11 8.26 15.16 1.6e-10 0.852 0.000 0.000 0.013 +6.90 [+3.40,+10.41]
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6-10 10 5.97 4.83 3.9e-05 0.007 0.584 0.585 0.643 -1.14 [-5.31,+3.03]
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6-13 10 2.83 4.47 0.00071 0.012 0.196 0.196 0.333 +1.65 [-0.87,+4.17]
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Columns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite
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DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope
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(dayp|subject) model, the honest test (DF collapses toward the animal count; 'n/a' if it
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did not converge). The early phase carries the Box-B2 faster-acquisition signal; late
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phases converge.
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==============================================================================
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FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
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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 11
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Covariance parameters 2
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Formula:
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success ~ 1 + dayp*tDCS + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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532.27 545.32 -260.14 520.27
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 9.046 4.8672 1.8586 61 0.067913
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{'dayp' } 8.2602 1.0773 7.6677 61 1.6422e-10
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{'tDCS' } -1.4984 7.9706 -0.188 61 0.8515
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{'dayp:tDCS' } 6.9041 1.7525 3.9397 61 0.00021248
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Lower Upper
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-0.68649 18.779
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6.106 10.414
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-17.437 14.44
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3.3998 10.408
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Random effects covariance parameters (95% CIs):
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Group: subject (11 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.4501
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Lower Upper
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5.5672 16.041
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 11.565 9.5751 13.968
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==============================================================================
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FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
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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 50
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Fixed effects coefficients 4
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Random effects coefficients 10
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Covariance parameters 2
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Formula:
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success ~ 1 + dayp*tDCS + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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404.12 415.59 -196.06 392.12
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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 5.345 11.618 46 2.7953e-15
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{'dayp' } 5.9667 1.3111 4.5509 46 3.8983e-05
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{'tDCS' } 24 8.4512 2.8398 46 0.0067003
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{'dayp:tDCS' } -1.1417 2.073 -0.55073 46 0.58449
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Lower Upper
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51.341 72.859
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3.3276 8.6058
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6.9887 41.011
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-5.3144 3.0311
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Random effects covariance parameters (95% CIs):
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Group: subject (10 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 10.466
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Lower Upper
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6.207 17.646
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.156 8.1572 12.644
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==============================================================================
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FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
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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 73
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Fixed effects coefficients 4
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Random effects coefficients 10
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Covariance parameters 2
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Formula:
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success ~ 1 + dayp*tDCS + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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593.06 606.81 -290.53 581.06
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 66.814 4.8749 13.706 69 2.2545e-21
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{'dayp' } 2.8256 0.79686 3.5459 69 0.00070793
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{'tDCS' } 19.873 7.7041 2.5796 69 0.012026
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{'dayp:tDCS' } 1.648 1.2621 1.3058 69 0.19595
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Lower Upper
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57.089 76.539
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1.2359 4.4153
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4.504 35.242
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-0.86972 4.1658
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Random effects covariance parameters (95% CIs):
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Group: subject (10 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.246
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Lower Upper
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5.4692 15.631
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 11.498 9.661 13.686
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