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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+==============================================================================
+PAPER LME REPLICATION -- mergeA2
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 8 rats, 109 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..14
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+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:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    962.68    978.83    -475.34          950.68  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF     pValue    
+    {'(Intercept)'}         20.701      4.9127     4.2138    105    5.3256e-05
+    {'day'        }         6.8599     0.69872     9.8178    105    1.5726e-16
+    {'stim'       }         22.092      6.4372     3.4319    105    0.00085848
+    {'day:stim'   }        -2.1793     0.81821    -2.6635    105     0.0089525
+
+
+    Lower      Upper   
+      10.96      30.442
+     5.4745      8.2453
+     9.3279      34.855
+    -3.8016    -0.55692
+
+Random effects covariance parameters (95% CIs):
+Group: rat (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1043e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        18.954      16.597    21.644
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(105)= -2.66  F(1)=   7.094  p=0.008953
+day (learning)             t(105)=  9.82  F(1)=  96.388  p=1.573e-16
+stim (main, Day 1)         t(105)=  3.43  F(1)=  11.778  p=0.0008585
+interaction 95% CI: [-3.80, -0.56]
+
+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.