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: mergeB2_full
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==============================================================================
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merge key: mergeB2 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 5 78 78.4 0.596 22
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Electrode-Box-A2 3 31 55.4 0.448 13
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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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565.84 585.16 -276.92 553.84
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 4.4986 0.054066 83.206 180
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{'group_Electrode-Box-A2'} -0.096187 0.09052 -1.0626 180
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{'group_Naive' } -0.34448 0.080981 -4.2538 180
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{'day_c' } 0.10082 0.0023113 43.621 180
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{'day_c2' } -0.00647 0.00024877 -26.008 180
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pValue Lower Upper
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1.3263e-145 4.3919 4.6053
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0.28939 -0.2748 0.08243
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3.3739e-05 -0.50427 -0.18468
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1.2306e-97 0.096261 0.10538
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7.2052e-63 -0.0069609 -0.0059791
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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'} 0.1166
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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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661.88 681.14 -324.94 649.88
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 0.57975 0.098046 5.913 178
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{'group_Electrode-Box-A2'} -0.33262 0.16122 -2.0631 178
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{'group_Naive' } -0.60174 0.14641 -4.1098 178
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{'day_c' } 0.12534 0.0034238 36.607 178
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{'day_c2' } -0.0069519 0.00035349 -19.666 178
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pValue Lower Upper
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1.6792e-08 0.38627 0.77323
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0.040554 -0.65077 -0.014466
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6.0348e-05 -0.89067 -0.31281
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9.0274e-85 0.11858 0.13209
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1.6927e-46 -0.0076494 -0.0062543
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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'} 0.21289
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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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560.22 585.99 -272.11 544.22
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.516 0.055699 81.079
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{'group_Electrode-Box-A2' } -0.11558 0.094376 -1.2247
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{'group_Naive' } -0.38342 0.083677 -4.5821
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{'day_c' } 0.092386 0.0030001 30.794
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{'day_c2' } -0.006943 0.00027446 -25.297
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{'group_Electrode-Box-A2:day_c'} 0.0036285 0.0081693 0.44417
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{'group_Naive:day_c' } 0.022361 0.0042611 5.2476
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DF pValue Lower Upper
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178 1.781e-142 4.4061 4.6259
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178 0.22231 -0.30182 0.070658
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178 8.6367e-06 -0.54854 -0.21829
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178 3.1867e-73 0.086466 0.098307
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178 7.6668e-61 -0.0074846 -0.0064014
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178 0.65746 -0.012493 0.01975
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178 4.3478e-07 0.013952 0.03077
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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'} 0.12017
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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=6.98, Box-A2 mean=8.19
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Welch two-sided p=0.508, Mann-Whitney p=0.786 (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=0.0000, 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 = 1.10x Box-A2 (one-sided p=0.1440) -> not supported
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[rate/level ] Box-B2 = 1.39x Box-A2 (one-sided p=0.0196) -> 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=0.0204, Mann-Whitney one-sided (B2>A2) p=0.0179
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rate (per-subject pooled success/total): Welch two-sided p=0.0440, Mann-Whitney one-sided (B2>A2) p=0.0179
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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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