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: unmerge_d6_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-A2
+N = 5 rats, 25 sessions   raw day coverage: treat 6..10, control 6..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              25
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    196.97    204.29    -92.486          184.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}          74.2      6.4017     11.591    21    1.3774e-10
+    {'day'        }           2.4      1.9295     1.2439    21       0.22726
+    {'stim'       }        14.333      8.2646     1.7343    21      0.097522
+    {'day:stim'   }        1.7667       2.491    0.70923    21       0.48598
+
+
+    Lower      Upper 
+     60.887    87.513
+    -1.6126    6.4126
+    -2.8539    31.521
+    -3.4136    6.9469
+
+Random effects covariance parameters (95% CIs):
+Group: rat (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.1065  
+
+
+    Lower     Upper 
+    2.5428    14.665
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        8.6289      6.3295    11.764
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(21)=  0.71  F(1)=   0.503  p=0.486
+day (learning)             t(21)=  1.24  F(1)=   1.547  p=0.2273
+stim (main, window start)  t(21)=  1.73  F(1)=   3.008  p=0.09752
+interaction 95% CI: [-3.41, +6.95]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.486, slope diff=+1.77)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.