analysis(matlab): per-variation subfolders (grouping x window) for the paper LME

Add make_variations.m + variation_analyze.m generating 20 self-contained subfolders under analysis/matlab/variations/, one per grouping x day-window, each with exactly three files: data.csv (curated subset), analyze.m (a simple standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv. Groupings (stim=1 / stim=0, other groups dropped): unmerge B2 vs A2 right_only B2+Right vs A2 (Box-A dropped) naive_a2 B2 vs A2+Naive (Right, Box-A dropped) naive_boxa B2 vs A2+Naive+Box-A (Right dropped) Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-20 20:49:54 -04:00
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+==============================================================================
+VARIATION: right_only_d0_13
+==============================================================================
+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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 84 sessions   raw day coverage: treat 0..13, control 0..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              84
+    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
+    695.18    709.77    -341.59          683.18  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}          21.7       4.3761     4.9587    80    3.9065e-06
+    {'day'        }        6.4863      0.68669     9.4459    80    1.1708e-14
+    {'stim'       }        3.5986       5.6847    0.63303    80       0.52852
+    {'day:stim'   }        1.7002      0.84679     2.0078    80      0.048042
+
+
+    Lower       Upper 
+      12.991    30.409
+      5.1198    7.8529
+     -7.7143    14.911
+    0.015009    3.3853
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        3.1353e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        14.12       12.139    16.425
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(80)=  2.01  F(1)=   4.031  p=0.04804
+day (learning)             t(80)=  9.45  F(1)=  89.224  p=1.171e-14
+stim (main, window start)  t(80)=  0.63  F(1)=   0.401  p=0.5285
+interaction 95% CI: [+0.02, +3.39]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.04804, slope diff=+1.70)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.