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_unmerged_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 71 sessions
+day coverage: Box-B2 0..14, 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.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              71
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    596.33    609.9    -292.16          584.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          21.53      5.5382    3.8874    67    0.00023506
+    {'day'        }         6.6587     0.72367    9.2014    67    1.6706e-13
+    {'tDCS'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:tDCS'   }        0.93747      0.9187    1.0204    67        0.3112
+
+
+    Lower       Upper 
+      10.475    32.584
+      5.2143    8.1032
+     -7.3444    23.385
+    -0.89627    2.7712
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7931  
+
+
+    Lower     Upper 
+    2.4723    13.574
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        14.163      11.93    16.813
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
+days (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
+tDCS (main, at Day 1)       t(67)=  1.04  F(1)=   1.086  p=0.3012
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.3112, slope diff=0.94) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=1.7e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3012) -> 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.)