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: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 42 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              42
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    331.16    341.59    -159.58          319.16  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.1493      2.4321    38      0.019831
+    {'day'        }         9.8571     1.3751      7.1681    38    1.4583e-08
+    {'stim'       }        -4.9762     6.8119    -0.73051    38       0.46956
+    {'day:stim'   }         5.3071     1.8191      2.9174    38     0.0058973
+
+
+    Lower      Upper 
+     2.0996    22.948
+     7.0733    12.641
+    -18.766    8.8138
+     1.6245    8.9898
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.2482  
+
+
+    Lower     Upper 
+    2.2427    12.282
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        9.9638      7.8829    12.594
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.92  F(1)=   8.511  p=0.005897
+day (learning)             t(38)=  7.17  F(1)=  51.382  p=1.458e-08
+stim (main, window start)  t(38)= -0.73  F(1)=   0.534  p=0.4696
+interaction 95% CI: [+1.62, +8.99]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.31)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.