[R] Huge speed performance difference when using non-trivial fixed effects in NLMER vs NLME

[Ramiro Barrantes]

Following Ben Bolker's methodology (described here https://rpubs.com/bbolker/3423) I incorporated non-trivial fixed effects in NLMER for a four-parameter logistic.   I placed a reproducible example here: https://rpubs.com/ramirob/648103


To summarize the question, if we have a dataset with individuals in groups where we have group-specific fixed effects, NLME's performance remains the same:

## [1] "NLME Time Required for data2Groups: 0.0458040237426758"

fit3Groups <- fitNLME(data3Groups,initialValues3Groups)

## [1] "NLME Time Required for data3Groups: 0.0375699996948242"

fit4Groups <- fitNLME(data4Groups,initialValues4Groups)

## [1] "NLME Time Required for data4Groups: 0.0526559352874756"

fit5Groups <- fitNLME(data5Groups,initialValues5Groups)

## [1] "NLME Time Required for data5Groups: 0.0502560138702393"


But when we do the analogous thing in NLMER, the performance increases with increasing number of groups:


## [1] "Time required for the data2Groups: 0.404773950576782"

fitNlmer3Groups <- fitNlmer(data3Groups, initialValues3Groups)

## [1] "Time required for the data3Groups: 0.579570055007935"

fitNlmer4Groups <- fitNlmer(data4Groups, initialValues4Groups)

## [1] "Time required for the data4Groups: 0.957509994506836"

fitNlmer5Groups <- fitNlmer(data5Groups, initialValues5Groups)

## [1] "Time required for the data5Groups: 1.68412184715271"


In addition, NLMER is much slower in general.  This is just a short example, but for more complicated cases the differences in performance are huge (minutes vs seconds).


Is NLMER "worth the wait" (e.g. less fragile, better convergence, etc) when trying to do non-trivial fixed effects? Is there a better methodology than the one described by Ben Bolker back in 2013?


Any insight appreciated.  Again, you can see a reproducible example here https://rpubs.com/ramirob/648103

Thank you!




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