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_5
+==============================================================================
+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, 36 sessions   raw day coverage: treat 0..5, control 0..5
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              36
+    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
+    289.66    299.16    -138.83          277.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.2775      2.3731    32      0.023816
+    {'day'        }         9.8571     1.4772      6.6729    32    1.5706e-07
+    {'stim'       }        -3.0159     7.4635    -0.40408    32       0.68884
+    {'day:stim'   }         5.3619      2.089      2.5667    32      0.015149
+
+
+    Lower      Upper 
+     1.7739    23.274
+     6.8482    12.866
+    -18.219    12.187
+     1.1067    9.6172
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.8527  
+
+
+    Lower     Upper 
+    1.7069    13.796
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.703      8.3104    13.785
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(32)=  2.57  F(1)=   6.588  p=0.01515
+day (learning)             t(32)=  6.67  F(1)=  44.528  p=1.571e-07
+stim (main, window start)  t(32)= -0.40  F(1)=   0.163  p=0.6888
+interaction 95% CI: [+1.11, +9.62]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.01515, slope diff=+5.36)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.