analysis(matlab): Box-A pooling variations (b2 vs a2+a, b2+a vs a2)

Add make_boxa_variations.m producing 6 variation folders (data.csv + analyze.m + result.txt) for the paper LME over windows 0-5, 6-10, 0-10: boxa_a2 B2 vs A2 + Box-A (b2 vs a2+a) boxa_b2 B2 + Box-A vs A2 (b2+a vs a2) plus variations/boxa_summary.csv (residual / Satterthwaite / random-slope interaction p). Early window (0-5) is obs-level significant (res p~0.03-0.04) but n.s. under the honest random-slope test (rs p~0.13); later windows n.s. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-23 13:39:01 -04:00
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+==============================================================================
+VARIATION: boxa_a2_d0_5
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A, Electrode-Box-A2
+N = 7 rats, 42 sessions   raw day coverage: treat 0..5, control 0..5
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              42
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    339.96    350.39    -163.98          327.96  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat        DF    pValue    
+    {'(Intercept)'}           10.06    4.3445       2.3155    38      0.026083
+    {'day'        }          10.543    1.4349       7.3472    38    8.3775e-09
+    {'stim'       }        -0.55159    6.6363    -0.083116    38        0.9342
+    {'day:stim'   }          4.6762    2.1919       2.1334    38       0.03941
+
+
+    Lower      Upper 
+     1.2645    18.855
+      7.638    13.448
+    -13.986    12.883
+     0.2389    9.1135
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        12.006      9.6941    14.868
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.13 F(1)=  4.551  p=0.03941    p=0.03878 (df=42)
+day (learning)             t(38)=  7.35 F(1)= 53.982  p=8.377e-09    p=4.651e-09 (df=42)
+stim (main, window start)  t(38)= -0.08 F(1)=  0.007  p=0.9342    p=0.9342 (df=42)
+interaction 95% CI: [+0.24, +9.11]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=2.87, p=0.1341
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.03941, slope diff=+4.68)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.