feat(matlab): print full native fitlme/fitglme summary in every scenario
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>
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Experiments DB Dev
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==============================================================================
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PHASED days x tDCS LME -- mergeB2 (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 8 9.86 14.65 2.9e-08 0.399 0.013 +4.79 [+1.06, +8.53]
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6-10 7 2.40 4.80 0.18 0.142 0.253 +2.40 [-1.80, +6.60]
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6-13 7 1.71 4.42 0.083 0.127 0.018 +2.71 [+0.50, +4.93]
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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 48
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Fixed effects coefficients 4
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Random effects coefficients 8
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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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383.06 394.29 -185.53 371.06
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 12.524 5.5254 2.2666 44 0.028385
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{'dayp' } 9.8571 1.4654 6.7268 44 2.8783e-08
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{'tDCS' } -5.9524 6.9892 -0.85166 44 0.39902
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{'dayp:tDCS' } 4.7943 1.8536 2.5865 44 0.013079
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Lower Upper
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1.3881 23.66
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6.9039 12.81
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-20.038 8.1334
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1.0587 8.5299
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Random effects covariance parameters (95% CIs):
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Group: subject (8 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.7044
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Lower Upper
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2.6066 12.484
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.618 8.5282 13.219
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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 35
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Fixed effects coefficients 4
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Random effects coefficients 7
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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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264.22 273.56 -126.11 252.22
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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 5.8956 12.586 31 1.0133e-13
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{'dayp' } 2.4 1.7415 1.3781 31 0.17803
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{'tDCS' } 10.52 6.9757 1.5081 31 0.14166
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{'dayp:tDCS' } 2.4 2.0606 1.1647 31 0.25302
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Lower Upper
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62.176 86.224
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-1.1518 5.9518
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-3.7071 24.747
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-1.8026 6.6026
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Random effects covariance parameters (95% CIs):
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Group: subject (7 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.7551
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Lower Upper
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2.7955 11.848
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.7883 5.9937 10.12
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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 50
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Fixed effects coefficients 4
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Random effects coefficients 7
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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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362.57 374.04 -175.28 350.57
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 75.168 5.6188 13.378 46 1.8212e-17
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{'dayp' } 1.712 0.96716 1.7701 46 0.083338
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{'tDCS' } 10.291 6.6299 1.5522 46 0.12747
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{'dayp:tDCS' } 2.7116 1.1009 2.4631 46 0.017575
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Lower Upper
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63.858 86.478
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-0.23482 3.6588
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-3.0544 23.636
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0.49565 4.9275
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Random effects covariance parameters (95% CIs):
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Group: subject (7 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 6.379
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Lower Upper
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3.4793 11.695
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.0508 5.7111 8.7047
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