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_d0_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 = 6 rats, 61 sessions   raw day coverage: treat 0..10, control 0..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              61
+    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
+    499.59    512.25    -243.79          487.59  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        17.372       5.1906     3.3469    57     0.0014518
+    {'day'        }        7.9666      0.79053     10.077    57    2.8317e-14
+    {'stim'       }        4.9763       7.2862    0.68298    57       0.49739
+    {'day:stim'   }        1.5577       1.0483     1.4858    57       0.14283
+
+
+    Lower      Upper 
+     6.9783    27.766
+     6.3836    9.5496
+     -9.614    19.567
+    -0.5416    3.6569
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.3536  
+
+
+    Lower     Upper 
+    2.1542    13.305
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        12.508      10.37    15.086
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(57)=  1.49  F(1)=   2.208  p=0.1428
+day (learning)             t(57)= 10.08  F(1)= 101.556  p=2.832e-14
+stim (main, window start)  t(57)=  0.68  F(1)=   0.466  p=0.4974
+interaction 95% CI: [-0.54, +3.66]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1428, slope diff=+1.56)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.