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>
200 lines
8.7 KiB
Plaintext
200 lines
8.7 KiB
Plaintext
==============================================================================
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tDCS GLM report -- scenario: mergeB2_d0_10
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==============================================================================
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merge key: mergeB2 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 5 55 66.4 0.533 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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==============================================================================
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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 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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297.84 314.85 -142.92 285.84
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 4.2369 0.08452 50.129 121
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{'group_Electrode-Box-A2'} -0.091837 0.13759 -0.66748 121
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{'group_Naive' } -0.39133 0.12596 -3.1068 121
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{'day_c' } 0.2019 0.0052735 38.287 121
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{'day_c2' } -0.024069 0.0015714 -15.317 121
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pValue Lower Upper
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8.553e-83 4.0696 4.4042
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0.50574 -0.36423 0.18055
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0.0023571 -0.64071 -0.14196
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1.787e-69 0.19146 0.21235
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4.1977e-30 -0.02718 -0.020958
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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'} 0.18296
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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 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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364.15 381.07 -176.07 352.15
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 0.11026 0.11374 0.96938 119
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{'group_Electrode-Box-A2'} -0.26616 0.18448 -1.4427 119
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{'group_Naive' } -0.53698 0.16912 -3.1751 119
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{'day_c' } 0.21673 0.0069278 31.283 119
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{'day_c2' } -0.013726 0.0021988 -6.2424 119
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pValue Lower Upper
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0.33432 -0.11496 0.33548
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0.15172 -0.63144 0.09913
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0.0019073 -0.87186 -0.2021
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2.9856e-59 0.20301 0.23044
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6.8729e-09 -0.01808 -0.009372
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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'} 0.24468
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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 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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304.98 327.67 -144.49 288.98
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.2592 0.082682 51.513
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{'group_Electrode-Box-A2' } -0.10296 0.13455 -0.76522
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{'group_Naive' } -0.46886 0.1243 -3.7722
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{'day_c' } 0.1907 0.0067258 28.354
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{'day_c2' } -0.02454 0.0015791 -15.541
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{'group_Electrode-Box-A2:day_c'} -0.0023862 0.012062 -0.19783
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{'group_Naive:day_c' } 0.045036 0.010729 4.1975
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DF pValue Lower Upper
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119 3.2785e-83 4.0955 4.423
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119 0.44566 -0.36937 0.16346
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119 0.00025365 -0.71498 -0.22275
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119 9.114e-55 0.17738 0.20402
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119 2.0273e-30 -0.027667 -0.021413
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119 0.84352 -0.02627 0.021498
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119 5.2317e-05 0.023791 0.066281
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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'} 0.17816
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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=9.74, Box-A2 mean=8.96
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Welch two-sided p=0.517, Mann-Whitney p=0.571 (nB=5, 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=0.0001, but with just
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observation-level DF (df2=119) 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 = 1.10x Box-A2 (one-sided p=0.2522) -> not supported
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[rate/level ] Box-B2 = 1.30x Box-A2 (one-sided p=0.0745) -> not supported
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Per-animal (Box-B2 n=5 vs Box-A2 n=3), pure stats (no GLME):
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count (per-subject mean success): Welch two-sided p=0.0228, Mann-Whitney one-sided (B2>A2) p=0.0179
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rate (per-subject pooled success/total): Welch two-sided p=0.0943, Mann-Whitney one-sided (B2>A2) p=0.0714
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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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