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_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, Naive
+N = 9 rats, 65 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              65
+    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
+    535.5    548.54    -261.75          523.5   
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        66.813       5.1278      13.03    61    2.7998e-19
+    {'day'        }        2.8262      0.83395     3.3889    61     0.0012345
+    {'stim'       }        21.913       8.8928     2.4641    61      0.016568
+    {'day:stim'   }        1.1553       1.4855    0.77769    61       0.43976
+
+
+    Lower      Upper 
+      56.56    77.067
+     1.1586    4.4937
+     4.1309    39.695
+    -1.8152    4.1258
+
+Random effects covariance parameters (95% CIs):
+Group: rat (9 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.7589  
+
+
+    Lower     Upper 
+    5.6191    16.948
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        12.033      10.004    14.473
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(61)=  0.78  F(1)=   0.605  p=0.4398
+day (learning)             t(61)=  3.39  F(1)=  11.485  p=0.001235
+stim (main, window start)  t(61)=  2.46  F(1)=   6.072  p=0.01657
+interaction 95% CI: [-1.82, +4.13]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.16)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.