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_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-A2, Naive
+N = 10 rats, 59 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              59
+    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
+    489.28    501.74    -238.64          477.28  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         9.0584     5.0267      1.8021    55      0.077018
+    {'day'        }         8.2568     1.1279      7.3206    55    1.1254e-09
+    {'stim'       }        0.44958      9.048    0.049688    55       0.96055
+    {'day:stim'   }         6.9622     2.0162      3.4532    55     0.0010733
+
+
+    Lower      Upper 
+    -1.0153    19.132
+     5.9965    10.517
+    -17.683    18.582
+     2.9217    11.003
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.6432  
+
+
+    Lower     Upper 
+    5.5002    16.907
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        12.109      9.9317    14.763
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(55)=  3.45  F(1)=  11.924  p=0.001073
+day (learning)             t(55)=  7.32  F(1)=  53.591  p=1.125e-09
+stim (main, window start)  t(55)=  0.05  F(1)=   0.002  p=0.9606
+interaction 95% CI: [+2.92, +11.00]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.96)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.