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 -- mergeA2 (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 10.54 15.16 1.5e-10 0.693 0.014 +4.62 [+1.00, +8.24]
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6-10 7 3.17 4.83 0.035 0.127 0.390 +1.66 [-2.22, +5.53]
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6-13 7 2.92 4.47 0.00032 0.125 0.118 +1.55 [-0.41, +3.52]
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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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381.01 392.23 -184.5 369.01
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 10.06 4.465 2.253 44 0.029296
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{'dayp' } 10.543 1.27 8.3016 44 1.4943e-10
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{'tDCS' } -2.5119 6.3144 -0.3978 44 0.6927
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{'dayp:tDCS' } 4.6214 1.796 2.5731 44 0.013526
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Lower Upper
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1.061 19.058
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7.9834 13.102
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-15.238 10.214
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1.0018 8.2411
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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'} 4.5394
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Lower Upper
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1.7406 11.838
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.625 8.5345 13.229
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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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265.24 274.57 -126.62 253.24
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 75.867 4.9313 15.385 31 4.6291e-16
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{'dayp' } 3.1667 1.4366 2.2043 31 0.035054
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{'tDCS' } 10.233 6.5234 1.5687 31 0.12687
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{'dayp:tDCS' } 1.6583 1.9004 0.87262 31 0.38958
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Lower Upper
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65.809 85.924
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0.23676 6.0966
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-3.0713 23.538
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-2.2176 5.5342
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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'} 5.9837
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Lower Upper
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2.9396 12.18
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.8684 6.0554 10.224
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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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367.22 378.69 -177.61 355.22
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 76.4 4.9765 15.352 46 1.0096e-19
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{'dayp' } 2.9189 0.74973 3.8932 46 0.00031772
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{'tDCS' } 10.289 6.5773 1.5643 46 0.12459
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{'dayp:tDCS' } 1.554 0.97562 1.5928 46 0.11805
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Lower Upper
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66.382 86.417
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1.4097 4.428
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-2.9502 23.528
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-0.40985 3.5178
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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'} 7.1357
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Lower Upper
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3.9247 12.974
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 7.3202 5.9279 9.0395
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