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_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-A2, Naive
+N = 9 rats, 45 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              45
+    Fixed effects coefficients           4
+    Random effects coefficients          9
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    368.51    379.35    -178.25          356.51  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}          62.1      5.5902      11.109    41    6.1328e-14
+    {'day'        }        5.9667      1.3623      4.3798    41    8.0273e-05
+    {'stim'       }        26.433      9.6824        2.73    41     0.0092904
+    {'day:stim'   }          -1.8      2.3596    -0.76285    41       0.44992
+
+
+    Lower      Upper 
+      50.81     73.39
+     3.2154    8.7179
+     6.8793    45.987
+    -6.5653    2.9653
+
+Random effects covariance parameters (95% CIs):
+Group: rat (9 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        10.986  
+
+
+    Lower     Upper
+    6.3454    19.02
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        10.552      8.376    13.294
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(41)= -0.76  F(1)=   0.582  p=0.4499
+day (learning)             t(41)=  4.38  F(1)=  19.183  p=8.027e-05
+stim (main, window start)  t(41)=  2.73  F(1)=   7.453  p=0.00929
+interaction 95% CI: [-6.57, +2.97]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.80)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.