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_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 30 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              30
+    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
+    230.64    239.05    -109.32          218.64  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}         74.2       6.0309    12.303    26    2.4061e-12
+    {'day'        }          2.4       1.8291    1.3121    26       0.20096
+    {'stim'       }         11.9       7.3864    1.6111    26       0.11924
+    {'day:stim'   }        2.425       2.2402    1.0825    26       0.28898
+
+
+    Lower      Upper 
+     61.803    86.597
+    -1.3599    6.1599
+    -3.2829    27.083
+    -2.1799    7.0299
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7092  
+
+
+    Lower     Upper 
+    2.5487    12.789
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        8.1802      6.1646    10.855
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(26)=  1.08  F(1)=   1.172  p=0.289
+day (learning)             t(26)=  1.31  F(1)=   1.722  p=0.201
+stim (main, window start)  t(26)=  1.61  F(1)=   2.596  p=0.1192
+interaction 95% CI: [-2.18, +7.03]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.289, slope diff=+2.42)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.