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
41 changed files with 4637 additions and 7 deletions
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+==============================================================================
+PAPER LME REPLICATION -- unmerged
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 6 rats, 71 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..14, control(A2) 0..13
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+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:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+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
+    {'stim'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:stim'   }        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: rat (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
+------------------------------------------------------------------
+stim x day (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
+day (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
+stim (main, Day 1)         t(67)=  1.04  F(1)=   1.086  p=0.3012
+interaction 95% CI: [-0.90, +2.77]
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.