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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tDCS GLM report -- scenario: mergeA2_full
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==============================================================================
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merge key: mergeA2 day window: 0..26 observations: 185
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==============================================================================
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DESCRIPTIVES
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==============================================================================
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group n_subj n_sessions mean_success mean_rate max_day
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--------------------------------------------------------------------------
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Electrode-Box-B2 4 63 80.2 0.605 22
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Electrode-Box-A2 4 46 60.4 0.484 14
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Naive 4 76 59.6 0.492 26
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==============================================================================
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(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
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==============================================================================
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Generalized linear mixed-effects model fit by PL
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Model information:
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Number of observations 185
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Fixed effects coefficients 5
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Random effects coefficients 12
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Covariance parameters 1
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Distribution Poisson
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Link Log
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FitMethod MPL
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Formula:
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success ~ 1 + group + day_c + day_c2 + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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564.95 584.27 -276.47 552.95
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 4.5232 0.058089 77.867 180
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{'group_Electrode-Box-A2'} -0.1219 0.083374 -1.4621 180
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{'group_Naive' } -0.36897 0.082023 -4.4984 180
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{'day_c' } 0.10078 0.002307 43.684 180
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{'day_c2' } -0.0064744 0.00024857 -26.047 180
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pValue Lower Upper
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1.4699e-140 4.4086 4.6379
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0.14547 -0.28641 0.042619
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1.2247e-05 -0.53082 -0.20712
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9.6816e-98 0.096227 0.10533
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5.8173e-63 -0.0069649 -0.0059839
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Random effects covariance parameters:
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Group: subject (12 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 0.11179
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Group: Error
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Name Estimate
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{'sqrt(Dispersion)'} 1
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==============================================================================
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(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
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==============================================================================
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Generalized linear mixed-effects model fit by PL
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Model information:
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Number of observations 183
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Fixed effects coefficients 5
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Random effects coefficients 12
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Covariance parameters 1
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Distribution Binomial
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Link Logit
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FitMethod MPL
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Formula:
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success ~ 1 + group + day_c + day_c2 + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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661.66 680.91 -324.83 649.66
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 0.62325 0.10794 5.7739 178
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{'group_Electrode-Box-A2'} -0.33496 0.15306 -2.1884 178
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{'group_Naive' } -0.64512 0.15201 -4.2441 178
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{'day_c' } 0.12541 0.0034219 36.649 178
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{'day_c2' } -0.0069611 0.00035344 -19.695 178
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pValue Lower Upper
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3.3843e-08 0.41024 0.83626
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0.029941 -0.63701 -0.032914
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3.5275e-05 -0.94509 -0.34516
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7.5329e-85 0.11866 0.13216
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1.4145e-46 -0.0076585 -0.0062636
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Random effects covariance parameters:
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Group: subject (12 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 0.20951
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Group: Error
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Name Estimate
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{'sqrt(Dispersion)'} 1
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==============================================================================
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(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
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==============================================================================
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Generalized linear mixed-effects model fit by PL
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Model information:
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Number of observations 185
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Fixed effects coefficients 7
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Random effects coefficients 12
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Covariance parameters 1
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Distribution Poisson
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Link Log
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FitMethod MPL
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Formula:
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success ~ 1 + day_c2 + group*day_c + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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557.88 583.64 -270.94 541.88
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.5419 0.060057 75.626
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{'group_Electrode-Box-A2' } -0.13686 0.086298 -1.5859
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{'group_Naive' } -0.41002 0.085075 -4.8196
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{'day_c' } 0.090967 0.0032463 28.021
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{'day_c2' } -0.0068957 0.0002769 -24.904
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{'group_Electrode-Box-A2:day_c'} 0.0071293 0.0064228 1.11
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{'group_Naive:day_c' } 0.023329 0.0043517 5.3609
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DF pValue Lower Upper
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178 3.0366e-137 4.4233 4.6604
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178 0.11453 -0.30716 0.033436
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178 3.0699e-06 -0.57791 -0.24214
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178 3.6033e-67 0.084561 0.097373
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178 6.7558e-60 -0.0074422 -0.0063493
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178 0.2685 -0.0055454 0.019804
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178 2.5437e-07 0.014742 0.031917
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Random effects covariance parameters:
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Group: subject (12 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 0.11568
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Group: Error
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Name Estimate
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{'sqrt(Dispersion)'} 1
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Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
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Per-subject OLS slope of success vs day: Box-B2 mean=6.92, Box-A2 mean=7.95
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Welch two-sided p=0.531, Mann-Whitney p=0.886 (nB=4, nA=4) -> parallel learning (no slope difference detected)
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(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
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observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
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NOT the basis for the conclusion above.)
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==============================================================================
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INTERPRETATION
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==============================================================================
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Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
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Python reference's population-average GEE -- directions/magnitudes should agree,
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exact ratios and p-values will differ.
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Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
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[count/level] Box-B2 = 1.13x Box-A2 (one-sided p=0.0719) -> not supported
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[rate/level ] Box-B2 = 1.40x Box-A2 (one-sided p=0.0143) -> SUPPORTED
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Per-animal (Box-B2 n=4 vs Box-A2 n=4), pure stats (no GLME):
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count (per-subject mean success): Welch two-sided p=0.0348, Mann-Whitney one-sided (B2>A2) p=0.0286
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rate (per-subject pooled success/total): Welch two-sided p=0.0555, Mann-Whitney one-sided (B2>A2) p=0.0286
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(No unknown groups -- they were merged into the anchors; only the H2 anchor
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contrast applies.)
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==============================================================================
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CAVEATS
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==============================================================================
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- Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
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depending on merge), and -- in unmerged scenarios -- each unknown condition
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(Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
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comparison here as preliminary.
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- Single-subject classification: for a 1-subject unknown, 'matches anchor X'
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means 'not statistically distinguishable from X', NOT proof of equivalence;
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inference with a single subject in a group is fragile.
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- Count vs rate: 'success' alone is a raw count; the rate model
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(success/attempts) is the fairer accuracy comparison when attempt counts differ
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between groups.
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