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: mergeA2_d0_10
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==============================================================================
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merge key: mergeA2 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 4 44 68.3 0.541 10
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Electrode-Box-A2 4 39 54.0 0.452 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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297.29 314.31 -142.65 285.29
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 4.2656 0.092258 46.236 121
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{'group_Electrode-Box-A2'} -0.12664 0.12996 -0.97447 121
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{'group_Naive' } -0.41983 0.12988 -3.2324 121
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{'day_c' } 0.20187 0.0052718 38.293 121
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{'day_c2' } -0.024068 0.0015714 -15.317 121
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pValue Lower Upper
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9.3543e-79 4.083 4.4483
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0.33177 -0.38392 0.13065
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0.0015818 -0.67696 -0.1627
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1.7547e-69 0.19144 0.21231
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4.2028e-30 -0.027179 -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'} 0.17893
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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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363.74 380.67 -175.87 351.74
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF
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{'(Intercept)' } 0.15282 0.1244 1.2285 119
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{'group_Electrode-Box-A2'} -0.28467 0.17474 -1.6291 119
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{'group_Naive' } -0.57945 0.17473 -3.3162 119
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{'day_c' } 0.21677 0.0069268 31.294 119
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{'day_c2' } -0.013732 0.0021989 -6.2451 119
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pValue Lower Upper
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0.22169 -0.0935 0.39915
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0.10595 -0.63068 0.061343
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0.0012101 -0.92543 -0.23346
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2.8838e-59 0.20305 0.23048
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6.7844e-09 -0.018086 -0.0093781
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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'} 0.23963
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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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304.66 327.35 -144.33 288.66
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat
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{'(Intercept)' } 4.2968 0.091824 46.794
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{'group_Electrode-Box-A2' } -0.15174 0.12947 -1.172
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{'group_Naive' } -0.50686 0.13029 -3.8902
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{'day_c' } 0.18436 0.0072535 25.417
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{'day_c2' } -0.024459 0.001577 -15.509
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{'group_Electrode-Box-A2:day_c'} 0.014228 0.010876 1.3082
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{'group_Naive:day_c' } 0.051203 0.011091 4.6167
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DF pValue Lower Upper
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119 1.7772e-78 4.115 4.4786
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119 0.24354 -0.4081 0.10462
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119 0.00016554 -0.76486 -0.24887
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119 6.5517e-50 0.17 0.19872
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119 2.3835e-30 -0.027581 -0.021336
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119 0.19332 -0.0073071 0.035763
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119 9.9271e-06 0.029242 0.073164
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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'} 0.17742
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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=9.71, Box-A2 mean=9.18
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Welch two-sided p=0.556, Mann-Whitney p=0.686 (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=0.0001, 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 = 1.14x Box-A2 (one-sided p=0.1649) -> not supported
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[rate/level ] Box-B2 = 1.33x Box-A2 (one-sided p=0.0517) -> not 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=0.0230, Mann-Whitney one-sided (B2>A2) p=0.0143
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rate (per-subject pooled success/total): Welch two-sided p=0.0689, Mann-Whitney one-sided (B2>A2) p=0.0571
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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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