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: naive_boxa_d0_10
+==============================================================================
+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, Naive
+N = 11 rats, 115 sessions   raw day coverage: treat 0..10, control 0..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+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
+    958.48    974.95    -473.24          946.48  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}        10.139       4.1642    2.4349    111      0.016488
+    {'day'        }        8.3463      0.49368    16.906    111    1.7432e-32
+    {'stim'       }        12.209       7.8948    1.5465    111       0.12484
+    {'day:stim'   }        1.1779      0.90166    1.3064    111       0.19412
+
+
+    Lower       Upper 
+      1.8878    18.391
+      7.3681    9.3246
+     -3.4352    27.853
+    -0.60877    2.9646
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.6713  
+
+
+    Lower    Upper 
+    5.112    14.709
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.706      11.962    15.704
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(111)=  1.31  F(1)=   1.707  p=0.1941
+day (learning)             t(111)= 16.91  F(1)= 285.824  p=1.743e-32
+stim (main, window start)  t(111)=  1.55  F(1)=   2.392  p=0.1248
+interaction 95% CI: [-0.61, +2.96]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.18)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.