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: unmerged_d0_10
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==============================================================================
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merge key: unmerged day window: 0..10 observations: 126
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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 3 33 70.0 0.555 10
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Electrode-Box-A 1 11 58.9 0.499 10
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Electrode-Box-A2 3 28 52.1 0.433 10
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Naive 4 43 46.8 0.440 10
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Right-Electrode 1 11 63.4 0.499 10
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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 126
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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 + 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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301.01 323.7 -142.51 285.01
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 4.2892 0.10522 40.765 119
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{'group_Electrode-Box-A' } -0.16759 0.20935 -0.80052 119
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{'group_Electrode-Box-A2'} -0.14442 0.14887 -0.97013 119
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{'group_Naive' } -0.44336 0.13882 -3.1938 119
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{'group_Right-Electrode' } -0.094696 0.20909 -0.45289 119
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{'day_c' } 0.20188 0.0052727 38.287 119
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{'day_c2' } -0.024069 0.0015714 -15.317 119
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pValue Lower Upper
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9.1655e-72 4.0809 4.4976
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0.425 -0.58213 0.24695
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0.33395 -0.43919 0.15035
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0.0017973 -0.71824 -0.16848
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0.65145 -0.50872 0.31933
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9.4346e-69 0.19144 0.21232
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6.4425e-30 -0.02718 -0.020957
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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'} 0.17716
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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 124
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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 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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366.74 389.3 -175.37 350.74
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 0.21772 0.13736 1.585 117
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{'group_Electrode-Box-A' } -0.27791 0.2726 -1.0195 117
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{'group_Electrode-Box-A2'} -0.37423 0.19356 -1.9334 117
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{'group_Naive' } -0.64393 0.18075 -3.5625 117
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{'group_Right-Electrode' } -0.25889 0.27218 -0.95118 117
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{'day_c' } 0.21668 0.0069264 31.283 117
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{'day_c2' } -0.013737 0.0021988 -6.2475 117
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pValue Lower Upper
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0.11567 -0.054325 0.48976
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0.31008 -0.81779 0.26196
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0.0556 -0.75756 0.0091015
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0.0005325 -1.0019 -0.28596
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0.34348 -0.79792 0.28014
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1.1453e-58 0.20296 0.2304
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6.9755e-09 -0.018092 -0.0093824
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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'} 0.22892
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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 126
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Fixed effects coefficients 11
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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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305.19 339.23 -140.6 281.19
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.3338 0.10278 42.165
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{'group_Electrode-Box-A' } -0.23805 0.20569 -1.1574
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{'group_Electrode-Box-A2' } -0.17701 0.14533 -1.218
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{'group_Naive' } -0.54258 0.13655 -3.9736
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{'group_Right-Electrode' } -0.15185 0.20519 -0.74005
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{'day_c' } 0.17638 0.0080904 21.802
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{'day_c2' } -0.024639 0.0015803 -15.591
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{'group_Electrode-Box-A:day_c' } 0.044079 0.016893 2.6093
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{'group_Electrode-Box-A2:day_c'} 0.011922 0.012896 0.92443
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{'group_Naive:day_c' } 0.059567 0.011689 5.0961
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{'group_Right-Electrode:day_c' } 0.036369 0.016336 2.2263
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DF pValue Lower Upper
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115 8.6925e-72 4.1302 4.5374
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115 0.24953 -0.64548 0.16937
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115 0.22572 -0.46489 0.11086
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115 0.00012391 -0.81305 -0.27211
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115 0.46078 -0.5583 0.25459
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115 1.1738e-42 0.16036 0.19241
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115 3.8845e-30 -0.027769 -0.021509
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115 0.010278 0.010617 0.07754
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115 0.3572 -0.013623 0.037467
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115 1.3744e-06 0.036414 0.08272
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115 0.027945 0.00401 0.068729
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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'} 0.17214
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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=9.52, Box-A2 mean=8.96
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Welch two-sided p=0.643, Mann-Whitney p=0.700 (nB=3, nA=3) -> 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=115) 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 = 1.16x Box-A2 (one-sided p=0.1660) -> not supported
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[rate/level ] Box-B2 = 1.45x Box-A2 (one-sided p=0.0266) -> SUPPORTED
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Per-animal (Box-B2 n=3 vs Box-A2 n=3), pure stats (no GLME):
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count (per-subject mean success): Welch two-sided p=0.0414, Mann-Whitney one-sided (B2>A2) p=0.0500
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rate (per-subject pooled success/total): Welch two-sided p=0.0702, Mann-Whitney one-sided (B2>A2) p=0.0500
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Classification -- is each UNKNOWN condition A2-like or B2-like?
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(ratio >1 = above that anchor; p = differs from that anchor)
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Electrode-Box-A:
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[count/level] vs Box-A2: 0.98x p=0.912 vs Box-B2: 0.85x p=0.425
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-> AMBIGUOUS (nearer Electrode-Box-A2)
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[rate/level ] vs Box-A2: 1.10x p=0.725 vs Box-B2: 0.76x p=0.310
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-> AMBIGUOUS (nearer Electrode-Box-A2)
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Right-Electrode:
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[count/level] vs Box-A2: 1.05x p=0.813 vs Box-B2: 0.91x p=0.651
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-> AMBIGUOUS (nearer Electrode-Box-A2)
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[rate/level ] vs Box-A2: 1.12x p=0.673 vs Box-B2: 0.77x p=0.343
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-> AMBIGUOUS (nearer Electrode-Box-A2)
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NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically
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distinguishable', weak evidence at n=1, not proof of equivalence.
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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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