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_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
+N = 6 rats, 70 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              70
+    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
+    586.72    600.22    -287.36          574.72  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}          21.7       4.5484     4.7709    66    1.0537e-05
+    {'day'        }        6.4863      0.71372     9.0881    66    3.0346e-13
+    {'stim'       }        6.0226       6.3135    0.95392    66        0.3436
+    {'day:stim'   }        1.5467      0.93755     1.6497    66       0.10376
+
+
+    Lower       Upper 
+      12.619    30.781
+      5.0614    7.9113
+     -6.5827    18.628
+    -0.32523    3.4185
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        14.676      12.436    17.32
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(66)=  1.65  F(1)=   2.721  p=0.1038
+day (learning)             t(66)=  9.09  F(1)=  82.593  p=3.035e-13
+stim (main, window start)  t(66)=  0.95  F(1)=   0.910  p=0.3436
+interaction 95% CI: [-0.33, +3.42]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1038, slope diff=+1.55)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.