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
parent 963fd889b2
commit 969c0205e8
63 changed files with 4343 additions and 0 deletions
@@ -0,0 +1,65 @@
+==============================================================================
+VARIATION: naive_a2_d0_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 = 10 rats, 104 sessions   raw day coverage: treat 0..10, control 0..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             104
+    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
+    869.17    885.04    -428.58          857.17  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue   
+    {'(Intercept)'}         10.33       4.4952     2.298    100     0.023646
+    {'day'        }        8.0905      0.53615     15.09    100    1.598e-27
+    {'stim'       }        12.019        8.117    1.4807    100      0.14184
+    {'day:stim'   }        1.4337      0.92893    1.5434    100      0.12589
+
+
+    Lower       Upper 
+      1.4115    19.248
+      7.0268    9.1542
+     -4.0853    28.123
+    -0.40926    3.2767
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.753   
+
+
+    Lower     Upper
+    5.0208    15.26
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.78       11.942    15.902
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(100)=  1.54  F(1)=   2.382  p=0.1259
+day (learning)             t(100)= 15.09  F(1)= 227.710  p=1.598e-27
+stim (main, window start)  t(100)=  1.48  F(1)=   2.192  p=0.1418
+interaction 95% CI: [-0.41, +3.28]
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1259, slope diff=+1.43)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.