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_d6_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 = 6 rats, 42 sessions   raw day coverage: treat 6..13, control 6..13
+(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          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    309.98    320.4    -148.99          297.98  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}        75.163      5.7598    13.049    38    1.2935e-15
+    {'day'        }        1.7153        1.01    1.6984    38      0.097608
+    {'stim'       }        11.525      7.0338    1.6386    38       0.10956
+    {'day:stim'   }        2.7579      1.1891    2.3193    38       0.02585
+
+
+    Lower       Upper 
+      63.503    86.823
+    -0.32925    3.7599
+     -2.7138    25.764
+     0.35071    5.1651
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.4688  
+
+
+    Lower     Upper
+    3.3423    12.52
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.3669      5.8525    9.2731
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.32  F(1)=   5.379  p=0.02585
+day (learning)             t(38)=  1.70  F(1)=   2.885  p=0.09761
+stim (main, window start)  t(38)=  1.64  F(1)=   2.685  p=0.1096
+interaction 95% CI: [+0.35, +5.17]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.02585, slope diff=+2.76)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.