GLMM in R versus SPSS (convergence and singularity problems vanish)

Question

Unfortunately, I had convergence (and singularity) issues when calculating my GLMM analysis models in R. When I tried it in SPSS, I got no such warning message and the results are only slightly different. Does it mean I can interpret the results from SPSS without worries? Or do I have to test for singularity/convergence issues to be sure?

Answer 1

You have two questions. I will answer both.

First Question

Does it mean I can interpret the results from SPSS without worries?

You do not want to do this. The reason being is that mixed models have a very specific parameterization. Here is a screenshot of common lme4 syntax from the original article about lme4 from the author:

With this comes assumptions about what your model is saying. If for example you are running a model with random intercepts only, you are assuming that the slopes do not vary by any measure. If you include correlated random slopes and random intercepts, you are then assuming that there is a relationship between the slopes and intercepts that may either be positive or negative. If you present this data as-is without knowing why it produced this summary, you may fail to explain your data in an accurate way.

The reason as highlighted by one of the comments is that SPSS runs off defaults whereas R requires explicit parameters for the model. I'm not surprised that the model failed to converge in R but not SPSS given that SPSS assumes no correlation between random slopes and intercepts. This kind of model is more likely to converge compared to a correlated model because the constraints that allow data to fit a correlated model make it very difficult to converge. However, without knowing how you modeled your data, it is impossible to actually know what the differences are. Perhaps if you provide an edit to your question that can be answered more directly, but just know that SPSS and R do not calculate these models the same way.

Second Question

Or do I have to test for singularity/convergence issues to be sure?

SPSS and R both have singularity checks as a default (check this page as an example). If your model fails to converge, you should drop it and use an alternative model (usually something that has a simpler random effects structure or improved optimization).