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_5
+==============================================================================
+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, 65 sessions   raw day coverage: treat 0..5, control 0..5
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              65
+    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
+    540.21    553.25    -264.1           528.21  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}        8.2006       4.622    1.7743    61      0.081011
+    {'day'        }        8.8157      1.0827    8.1421    61    2.5062e-11
+    {'stim'       }        1.3073      8.7275    0.1498    61       0.88142
+    {'day:stim'   }        6.4033      2.0341    3.1479    61     0.0025447
+
+
+    Lower      Upper 
+    -1.0416    17.443
+     6.6507    10.981
+    -16.144    18.759
+     2.3358    10.471
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.1032  
+
+
+    Lower     Upper 
+    5.2068    15.915
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        12.477      10.33    15.071
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(61)=  3.15  F(1)=   9.909  p=0.002545
+day (learning)             t(61)=  8.14  F(1)=  66.293  p=2.506e-11
+stim (main, window start)  t(61)=  0.15  F(1)=   0.022  p=0.8814
+interaction 95% CI: [+2.34, +10.47]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.002545, slope diff=+6.40)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.