Occasionally when fitting mixed effects models to categorical data using PROC GLIMMIX, the returned coefficient estimates for the specified fixed effects reference categories (e.g. cat3 in var1 from the statement class <var1>(ref = 'Cat3') <var2>(ref = 'Cat2') etc.) are non-zero. The coefficient estimates for all other categories of the variable are also shifted by the value of this non-zero estimate and this value is "counteracted" by the intercept. A completely made up toy example is set out below:   Expected behavior of fixed effects parameter estimate output:   Effect       Cat       Estimate    Standard Error    DF    t Value    Pr>|t|   Int.                        1.02           0.05                   100     22.30    <0.05 Var1         1          -0.02           0.08                   100   -0.31      >0.05 Var1         2          -1.04           0.04                   100   -24.32    <0.05 Var1         3           0                  .                         .         .              .  Var2         1           0.27          0.08                   100      3.11      <0.05  Var2         2           .                   .                        .         .               .    Actual "unusual" output:   Effect       Cat       Estimate    Standard Error    DF    t Value    Pr>|t|   Int.                         16.02           0.05                  100      122.30   <0.05 Var1         1          -15.02           0.08                   100    -100.31   <0.05 Var1         2          -16.04           0.04                   100    -124.32   <0.05 Var1         3          -15.0                  0                   100      Infty      <0.05  Var2         1            0.27            0.08                   100      3.11      <0.05  Var2         2           .                   .                        .         .               .    We can see from the above that an estimate for the reference category of Var1 has been produced by PROC GLIMMIX, the Intercept estimate has shifted in the opposite direction by the same value as this estimate and all other categories of Var1 have decreased by the value of the estimate. As a result of this the previously non-significant estimate for cat1 of Var one is now significant. There is no appreciable difference in any of the model fit stats    This feels to me like the parameter optimization has found a local minimum and the intercept is being used to offset this. As the relative difference between the chosen reference category and the other categories of the variable has remained constant it seems like the relative estimates may be sound, and the corresponding t-values and p-values could be re calculated in this basis.   From this perspective this unusual solution seems "fine" (as the expected behavior result confirms) but in any case I would like to understand this feature further.   I hope this makes sense! If anyone has any suggestions then I'd love to hear them. If you require any further information please do ask.   Best,   Dave   ... View more