I'm about to attempt to fit a non-linear mixed effects model $(A + B*e^t)$ in lme4. I've already tried fitting this model in nlme with some difficulty due to noise within the data. However, I understand that lme4 has more robust algorithms that's able to better fit non-linear models such as the one written above.
I've ready a few posts such as How to choose nlme or lme4 R library for mixed effects models?
My question: Is lme4 better than nlme with non-linear models? (nlme vs nlmer)
Don't know if this is discussed or formerly covered elsewhere, but the major differences are:
nlme): as with the lme vs. lmer comparison, nlme can be used in conjunction with the package's corStruct and varStruct capabilities to fit models with autocorrelation and heteroscedasticitynlme): nlme is well-documented in Pinheiro and Bates's 2000 Springer book.nlme): nlme makes it (much) easier to include non-trivial fixed effects in the model. As documented here, it is theoretically possible but quite difficult to include fixed effects; this is in large part because the derivatives have to be explicitly specified for nlmer.nlmer): the machinery underlying nlmer is more sophisticated/modern than that of nlme, which suggests that it should be faster and possibly more stable for large problems.Why don't you try it and see?
