feat(matlab): windowed paper-LME replication for mergeNaive (d0_10, d0_13)

Make tdcs_paper_lme window-aware (windowKey 'full'|'d0_10'|'d0_13', default 'full' -> unchanged filename). Windowed runs give the two anchor arms equal day coverage, so the stim:day interaction is not confounded by the full-range coverage imbalance; the report now states whether coverage is equal and adds an INTERPRETATION block. Add switch cases paper_mergeNaive_d0_10 / _d0_13 and include them in run_all. Finding: unlike mergeA2 (fair-window interaction n.s.), the pooled-naive control keeps the interaction significant in the fair d0_13 window (F(1)=7.07, p=0.0088, +1.80 [+0.46,+3.14]; equal on Day 1 p=0.20), reproducing the paper without the coverage confound; d0_10 is borderline (p=0.051). Tests: tLme asserts equal coverage / no warning / one full summary for both windows and a significant positive d0_13 interaction; bad windowKey errors. Suite 41/41. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-20 17:49:09 -04:00
parent 0e9263b958
commit 963fd889b2
9 changed files with 532 additions and 16 deletions
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+==============================================================================
+PAPER LME REPLICATION -- mergeNaive (days 0-10)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 11 rats, 115 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..10, control(A2) 0..10
+(Equal day coverage -- the stim:day interaction over this window is NOT
+ confounded by the full-range coverage imbalance.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             115
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    952.54    969.01    -470.27          940.54  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}         10.33       4.3573    2.3706    111      0.019482
+    {'day'        }        8.0907      0.51948    15.575    111    1.0806e-29
+    {'stim'       }        9.4432       7.1538      1.32    111       0.18954
+    {'day:stim'   }        1.6184       0.8216    1.9699    111      0.051345
+
+
+    Lower         Upper 
+        1.6953    18.964
+        7.0613      9.12
+       -4.7326    23.619
+    -0.0096134    3.2465
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.4877  
+
+
+    Lower     Upper 
+    5.0001    14.408
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.352      11.652    15.299
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(111)=  1.97  F(1)=   3.880  p=0.05134
+day (learning)             t(111)= 15.57  F(1)= 242.567  p=1.081e-29
+stim (main, Day 1)         t(111)=  1.32  F(1)=   1.742  p=0.1895
+interaction 95% CI: [-0.01, +3.25]
+
+INTERPRETATION
+  - stim x day interaction: n.s. (p=0.0513, slope diff=+1.62) -> slopes parallel -- no differential learning rate over this window.
+  - stim main effect on Day 1 (our day 0): n.s. (p=0.1895) -> groups are comparable on Day 1.
+
+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.