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_a2_d0_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
+control   (stim=0): Electrode-Box-A2, Naive
+N = 10 rats, 124 sessions   raw day coverage: treat 0..13, control 0..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    1055.4    1072.3    -521.69          1043.4  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}        16.252       4.4327    3.6664    120    0.00036779
+    {'day'        }        6.4053      0.43948    14.575    120    2.5469e-28
+    {'stim'       }         11.52        8.023    1.4359    120       0.15363
+    {'day:stim'   }        1.6137      0.77637    2.0785    120      0.039797
+
+
+    Lower       Upper 
+      7.4757    25.029
+      5.5352    7.2755
+     -4.3648    27.405
+    0.076509    3.1508
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.4599  
+
+
+    Lower     Upper 
+    4.8243    14.835
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        15.264      13.405    17.381
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(120)=  2.08  F(1)=   4.320  p=0.0398
+day (learning)             t(120)= 14.57  F(1)= 212.424  p=2.547e-28
+stim (main, window start)  t(120)=  1.44  F(1)=   2.062  p=0.1536
+interaction 95% CI: [+0.08, +3.15]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0398, slope diff=+1.61)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.