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
parent d59ff2b659
commit 0e9263b958
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+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 70 sessions
+day coverage: Box-B2 0..13, 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              70
+    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
+    586.72    600.22    -287.36          574.72  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}          21.7       4.5484     4.7709    66    1.0537e-05
+    {'day'        }        6.4863      0.71372     9.0881    66    3.0346e-13
+    {'tDCS'       }        6.0226       6.3135    0.95392    66        0.3436
+    {'day:tDCS'   }        1.5467      0.93755     1.6497    66       0.10376
+
+
+    Lower       Upper 
+      12.619    30.781
+      5.0614    7.9113
+     -6.5827    18.628
+    -0.32523    3.4185
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        14.676      12.436    17.32
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(66)=  1.65  F(1)=   2.721  p=0.1038
+days (learning)             t(66)=  9.09  F(1)=  82.593  p=3.035e-13
+tDCS (main, at Day 1)       t(66)=  0.95  F(1)=   0.910  p=0.3436
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1038, slope diff=1.55) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=3e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3436) -> 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.)