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>
71 lines
2.9 KiB
Plaintext
71 lines
2.9 KiB
Plaintext
==============================================================================
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LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_10
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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, 83 sessions
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day coverage: Box-B2 0..10, Box-A2 0..10
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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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==============================================================================
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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 83
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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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669.06 683.57 -328.53 657.06
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 17.318 5.1725 3.348 79 0.0012488
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{'day' } 7.9975 0.75795 10.551 79 9.547e-17
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{'tDCS' } 0.42782 6.5088 0.06573 79 0.94776
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{'day:tDCS' } 1.7407 0.91342 1.9057 79 0.060324
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Lower Upper
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7.0221 27.613
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6.4888 9.5061
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-12.528 13.383
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-0.07739 3.5588
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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'} 5.7075
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Lower Upper
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2.7829 11.705
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 11.955 10.184 14.034
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effect t (df) F (df1) p
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----------------------------------------------------------------------
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days x tDCS (interaction) t(79)= 1.91 F(1)= 3.632 p=0.06032
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days (learning) t(79)= 10.55 F(1)= 111.334 p=9.547e-17
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tDCS (main, at Day 1) t(79)= 0.07 F(1)= 0.004 p=0.9478
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INTERPRETATION
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- days x tDCS interaction: n.s. (p=0.0603, slope diff=1.74) -> slopes are parallel (no differential change over training).
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- days (learning): SIGNIFICANT (p=9.5e-17) -> performance improves with training.
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- tDCS main effect on Day 1 (our day 0): n.s. (p=0.9478) -> the groups are comparable (as in the paper) 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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