Why does SLS (REML) give a different intercept than Python statsmodels for the same mixed model?
I fitted: Y ~ A(Sum) + B(Sum) + C(Sum) + D(Sum) + Subject(&Random) in both JMP SLS (REML) and Python statsmodels MixedLM (REML) on the same data (~14k obs, ~200 levels of A, sparse A×C cross-tabulation, ~120 random subjects). What I observe: All ~200 LS Means for factor A are shifted by a constant ~0.22 (JMP higher)Relative effects and rankings are identical (diff std < 0.001)LS Means f...