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
==============================================================================
|
|
LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_full
|
|
==============================================================================
|
|
model: success ~ day * tDCS + (1|subject) [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
|
|
N = 8 subjects, 109 sessions
|
|
day coverage: Box-B2 0..22, Box-A2 0..13
|
|
(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
|
|
main effect below is the group difference on Day 1 -- comparable to the paper.)
|
|
** WARNING: the groups' day coverage is UNEQUAL (differ by 9 days). The
|
|
interaction/slope over this window EXTRAPOLATES the shorter group's line and is
|
|
CONFOUNDED -- prefer the _d0_13 window (both groups have data throughout). **
|
|
|
|
==============================================================================
|
|
FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
|
|
==============================================================================
|
|
|
|
Linear mixed-effects model fit by ML
|
|
|
|
Model information:
|
|
Number of observations 109
|
|
Fixed effects coefficients 4
|
|
Random effects coefficients 8
|
|
Covariance parameters 2
|
|
|
|
Formula:
|
|
success ~ 1 + day*tDCS + (1 | subject)
|
|
|
|
Model fit statistics:
|
|
AIC BIC LogLikelihood Deviance
|
|
967.24 983.39 -477.62 955.24
|
|
|
|
Fixed effects coefficients (95% CIs):
|
|
Name Estimate SE tStat DF pValue
|
|
{'(Intercept)'} 21.7 5.9981 3.6178 105 0.00045861
|
|
{'day' } 6.4863 0.9412 6.8916 105 4.2336e-10
|
|
{'tDCS' } 17.493 7.1288 2.4538 105 0.015779
|
|
{'day:tDCS' } -1.4651 1.025 -1.4294 105 0.15586
|
|
|
|
|
|
Lower Upper
|
|
9.8069 33.593
|
|
4.6201 8.3526
|
|
3.3578 31.628
|
|
-3.4974 0.56726
|
|
|
|
Random effects covariance parameters (95% CIs):
|
|
Group: subject (8 Levels)
|
|
Name1 Name2 Type Estimate
|
|
{'(Intercept)'} {'(Intercept)'} {'std'} 2.1487e-15
|
|
|
|
|
|
Lower Upper
|
|
NaN NaN
|
|
|
|
Group: Error
|
|
Name Estimate Lower Upper
|
|
{'Res Std'} 19.354 16.948 22.101
|
|
|
|
|
|
effect t (df) F (df1) p
|
|
----------------------------------------------------------------------
|
|
days x tDCS (interaction) t(105)= -1.43 F(1)= 2.043 p=0.1559
|
|
days (learning) t(105)= 6.89 F(1)= 47.494 p=4.234e-10
|
|
tDCS (main, at Day 1) t(105)= 2.45 F(1)= 6.021 p=0.01578
|
|
|
|
INTERPRETATION
|
|
- days x tDCS interaction: n.s. (p=0.1559, slope diff=-1.47) -> slopes are parallel (no differential change over training).
|
|
- days (learning): SIGNIFICANT (p=4.2e-10) -> performance improves with training.
|
|
- tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0158) -> the groups already DIFFER on Day 1.
|
|
|
|
Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
|
|
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.
|
|
(Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
|