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_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.)