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 -- unmerged (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 6 9.86 15.22 1.6e-07 0.689 0.015 +5.36 [+1.11, +9.62]
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6-10 5 2.40 4.17 0.23 0.098 0.486 +1.77 [-3.41, +6.95]
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6-13 5 1.71 3.98 0.12 0.101 0.097 +2.27 [-0.44, +4.97]
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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 36
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Fixed effects coefficients 4
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Random effects coefficients 6
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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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289.66 299.16 -138.83 277.66
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 12.524 5.2775 2.3731 32 0.023816
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{'dayp' } 9.8571 1.4772 6.6729 32 1.5706e-07
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{'tDCS' } -3.0159 7.4635 -0.40408 32 0.68884
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{'dayp:tDCS' } 5.3619 2.089 2.5667 32 0.015149
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Lower Upper
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1.7739 23.274
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6.8482 12.866
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-18.219 12.187
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1.1067 9.6172
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Random effects covariance parameters (95% CIs):
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Group: subject (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 4.8527
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Lower Upper
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1.7069 13.796
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.703 8.3104 13.785
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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 25
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Fixed effects coefficients 4
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Random effects coefficients 5
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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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196.97 204.29 -92.486 184.97
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 74.2 6.4017 11.591 21 1.3774e-10
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{'dayp' } 2.4 1.9295 1.2439 21 0.22726
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{'tDCS' } 14.333 8.2646 1.7343 21 0.097522
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{'dayp:tDCS' } 1.7667 2.491 0.70923 21 0.48598
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Lower Upper
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60.887 87.513
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-1.6126 6.4126
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-2.8539 31.521
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-3.4136 6.9469
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Random effects covariance parameters (95% CIs):
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Group: subject (5 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 6.1065
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Lower Upper
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2.5428 14.665
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 8.6289 6.3295 11.764
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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 34
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Fixed effects coefficients 4
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Random effects coefficients 5
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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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257.04 266.2 -122.52 245.04
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 75.17 6.2311 12.064 30 4.8879e-13
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{'dayp' } 1.7101 1.061 1.6117 30 0.11749
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{'tDCS' } 13.563 8.0252 1.69 30 0.1014
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{'dayp:tDCS' } 2.267 1.3237 1.7126 30 0.097101
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Lower Upper
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62.445 87.896
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-0.45682 3.877
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-2.8271 29.952
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-0.43635 4.9703
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Random effects covariance parameters (95% CIs):
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Group: subject (5 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 7.1166
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Lower Upper
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3.4834 14.539
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.7328 5.9847 9.9914
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