feat(matlab): print full native fitlme/fitglme summary in every scenario

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>
2026-07-20 14:21:34 -04:00
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+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 83 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(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.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              83
+    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
+    669.06    683.57    -328.53          657.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue   
+    {'(Intercept)'}         17.318      5.1725      3.348    79    0.0012488
+    {'day'        }         7.9975     0.75795     10.551    79    9.547e-17
+    {'tDCS'       }        0.42782      6.5088    0.06573    79      0.94776
+    {'day:tDCS'   }         1.7407     0.91342     1.9057    79     0.060324
+
+
+    Lower       Upper 
+      7.0221    27.613
+      6.4888    9.5061
+     -12.528    13.383
+    -0.07739    3.5588
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7075  
+
+
+    Lower     Upper 
+    2.7829    11.705
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.955      10.184    14.034
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(79)=  1.91  F(1)=   3.632  p=0.06032
+days (learning)             t(79)= 10.55  F(1)= 111.334  p=9.547e-17
+tDCS (main, at Day 1)       t(79)=  0.07  F(1)=   0.004  p=0.9478
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.0603, slope diff=1.74) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=9.5e-17) -> performance improves with training.
+  - 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.
+
+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.)