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
8.8 KiB
Plaintext
200 lines
8.8 KiB
Plaintext
==============================================================================
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tDCS GLM report -- scenario: mergeNaive_full
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==============================================================================
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merge key: mergeNaive 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-A 1 15 70.7 0.557 14
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Electrode-Box-A2 7 107 58.4 0.479 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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569.28 588.6 -278.64 557.28
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.5235 0.074032 61.102
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{'group_Electrode-Box-A' } -0.12346 0.16529 -0.74694
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{'group_Electrode-Box-A2'} -0.26481 0.093039 -2.8462
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{'day_c' } 0.10065 0.0023081 43.608
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{'day_c2' } -0.0064638 0.00024915 -25.944
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DF pValue Lower Upper
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180 2.6809e-122 4.3774 4.6696
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180 0.45607 -0.44962 0.2027
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180 0.0049387 -0.44839 -0.081217
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180 1.2924e-97 0.096096 0.10521
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180 1.0244e-62 -0.0069555 -0.0059722
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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'} 0.14464
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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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663.16 682.41 -325.58 651.16
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 0.62291 0.11692 5.3275
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{'group_Electrode-Box-A' } -0.21631 0.26048 -0.83042
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{'group_Electrode-Box-A2'} -0.5314 0.14632 -3.6318
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{'day_c' } 0.12505 0.003415 36.618
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{'day_c2' } -0.0069489 0.00035361 -19.651
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DF pValue Lower Upper
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178 2.981e-07 0.39218 0.85365
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178 0.40742 -0.73034 0.29772
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178 0.00036793 -0.82014 -0.24265
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178 8.6013e-85 0.11831 0.13179
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178 1.8602e-46 -0.0076467 -0.0062511
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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'} 0.22797
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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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561.97 587.73 -272.98 545.97
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.5372 0.083001 54.664
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{'group_Electrode-Box-A' } -0.13394 0.18535 -0.72264
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{'group_Electrode-Box-A2' } -0.27771 0.10424 -2.6641
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{'day_c' } 0.090249 0.0032246 27.988
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{'day_c2' } -0.0066704 0.00025769 -25.885
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{'group_Electrode-Box-A:day_c' } 0.010369 0.0086089 1.2044
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{'group_Electrode-Box-A2:day_c'} 0.020125 0.0041158 4.8897
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DF pValue Lower Upper
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178 3.3741e-113 4.3734 4.701
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178 0.47085 -0.4997 0.23182
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178 0.0084289 -0.48341 -0.071999
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178 4.2889e-67 0.083886 0.096613
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178 3.0498e-62 -0.0071789 -0.0061619
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178 0.23002 -0.0066197 0.027358
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178 2.2462e-06 0.012003 0.028247
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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'} 0.16287
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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=6.25
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Welch two-sided p=0.689, Mann-Whitney p=0.527 (nB=4, nA=7) -> 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 = 1.30x Box-A2 (one-sided p=0.0022) -> SUPPORTED
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[rate/level ] Box-B2 = 1.70x Box-A2 (one-sided p=0.0001) -> SUPPORTED
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Per-animal (Box-B2 n=4 vs Box-A2 n=7), pure stats (no GLME):
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count (per-subject mean success): Welch two-sided p=0.0158, Mann-Whitney one-sided (B2>A2) p=0.0030
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rate (per-subject pooled success/total): Welch two-sided p=0.0207, Mann-Whitney one-sided (B2>A2) p=0.0030
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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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