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_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-A, Electrode-Box-A2, Naive
+N = 10 rats, 50 sessions   raw day coverage: treat 6..10, control 6..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    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
+    405.22    416.69    -196.61          393.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}        64.543      5.1823      12.454    46    2.4341e-16
+    {'day'        }        5.7857      1.2123      4.7727    46    1.8769e-05
+    {'stim'       }         23.99      9.4616      2.5356    46      0.014689
+    {'day:stim'   }        -1.619      2.2133    -0.73152    46       0.46817
+
+
+    Lower      Upper 
+     54.111    74.974
+     3.3456    8.2259
+     4.9452    43.036
+    -6.0741     2.836
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        11.237  
+
+
+    Lower     Upper
+    6.7417    18.73
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.142      8.1466    12.627
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(46)= -0.73  F(1)=   0.535  p=0.4682
+day (learning)             t(46)=  4.77  F(1)=  22.779  p=1.877e-05
+stim (main, window start)  t(46)=  2.54  F(1)=   6.429  p=0.01469
+interaction 95% CI: [-6.07, +2.84]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4682, slope diff=-1.62)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.