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_d6_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-A, Electrode-Box-A2, Naive
+N = 10 rats, 73 sessions   raw day coverage: treat 6..13, control 6..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              73
+    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
+    595.91    609.65    -291.95          583.91  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}         68.651      4.9672     13.821    69    1.4737e-21
+    {'day'        }         3.1029     0.72917     4.2555    69    6.4463e-05
+    {'stim'       }         20.083      9.0854     2.2105    69      0.030392
+    {'day:stim'   }        0.87369      1.3869    0.62995    69       0.53081
+
+
+    Lower      Upper 
+     58.741     78.56
+     1.6483    4.5576
+      1.958    38.208
+    -1.8931    3.6405
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        10.755  
+
+
+    Lower    Upper 
+    6.51     17.768
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.525      9.6827    13.718
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(69)=  0.63  F(1)=   0.397  p=0.5308
+day (learning)             t(69)=  4.26  F(1)=  18.109  p=6.446e-05
+stim (main, window start)  t(69)=  2.21  F(1)=   4.886  p=0.03039
+interaction 95% CI: [-1.89, +3.64]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.87)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.