Experiments DB Dev
0e9263b958
Add tdcs_model_summary as the single place that renders a fitted model verbatim (disp(model) with the Command-Window <strong> markup stripped), and route every report through it so all scenarios emit the complete model-fitting output: - tdcs_report: adds the learning-rate GLMM's full summary (count/rate already had theirs); localCleanDisp now delegates to the shared helper. - tdcs_lme_report / tdcs_paper_lme: embed the full fitlme summary before the curated effect table. - tdcs_phase_lme: appends each phase's full fitlme summary. Tests: tReport asserts 3 native summaries in a GLMM report; tLme asserts the lme_*/paper_*/phase_* reports embed 1/1/3 summaries with markup stripped. Suite 39/39. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
156 lines
5.5 KiB
Plaintext
156 lines
5.5 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 A2slope B2slope day p tDCS(lvl) p interaction p 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 +6.90 [+3.40, +10.41]
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6-10 10 5.97 4.83 3.9e-05 0.007 0.584 -1.14 [-5.31, +3.03]
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6-13 10 2.83 4.47 0.00071 0.012 0.196 +1.65 [-0.87, +4.17]
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Note: the interaction p (and CI) use fitlme observation-level DF and are
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ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
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phase carries the Box-B2 faster-acquisition signal; late 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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