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>
74 lines
3.2 KiB
Plaintext
74 lines
3.2 KiB
Plaintext
==============================================================================
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LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_full
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==============================================================================
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model: success ~ day * tDCS + (1|subject) [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
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N = 8 subjects, 109 sessions
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day coverage: Box-B2 0..22, Box-A2 0..14
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(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
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main effect below is the group difference on Day 1 -- comparable to the paper.)
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** WARNING: the groups' day coverage is UNEQUAL (differ by 8 days). The
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interaction/slope over this window EXTRAPOLATES the shorter group's line and is
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CONFOUNDED -- prefer the _d0_14 window (both groups have data throughout). **
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==============================================================================
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FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 109
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Fixed effects coefficients 4
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Random effects coefficients 8
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Covariance parameters 2
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Formula:
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success ~ 1 + day*tDCS + (1 | subject)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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962.68 978.83 -475.34 950.68
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 20.701 4.9127 4.2138 105 5.3256e-05
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{'day' } 6.8599 0.69872 9.8178 105 1.5726e-16
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{'tDCS' } 22.092 6.4372 3.4319 105 0.00085848
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{'day:tDCS' } -2.1793 0.81821 -2.6635 105 0.0089525
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Lower Upper
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10.96 30.442
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5.4745 8.2453
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9.3279 34.855
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-3.8016 -0.55692
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Random effects covariance parameters (95% CIs):
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Group: subject (8 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 2.1043e-15
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Lower Upper
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NaN NaN
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 18.954 16.597 21.644
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effect t (df) F (df1) p
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----------------------------------------------------------------------
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days x tDCS (interaction) t(105)= -2.66 F(1)= 7.094 p=0.008953
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days (learning) t(105)= 9.82 F(1)= 96.388 p=1.573e-16
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tDCS (main, at Day 1) t(105)= 3.43 F(1)= 11.778 p=0.0008585
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INTERPRETATION
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- days x tDCS interaction: SIGNIFICANT (p=0.0090, slope diff=-2.18) -> the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows).
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- days (learning): SIGNIFICANT (p=1.6e-16) -> performance improves with training.
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- tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0009) -> the groups already DIFFER on Day 1.
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Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
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days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
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(Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
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