Does anyone have recommendations on steps you should take when your model fit is unable to successfully minimize? I saw the prior feed on failed minimization (Successful minimization:) and looked at my ft.optim, but I am not sure how to go about addressing it. Is there a systematic approach/things I can adjust that may lead to successful minimization?
I would recommend to fix some parameters that you believe they are similar to literature values. Example include the physiologic parameters in case of Friberg model (semiphysiologic model for neutrophil count). You can also reduce the number of random effects for less influential parameters (for example the etas on peripheral volume of distribution or intercompartmental clearance). Also there could be influential subjects or outliers in you data. I am not sure about your specific case, it will be helpful if you shared some context of your problem.
Sometimes it might also be that you are fine with the result even if the status of the minimization is
failed. It might be difficult to compute standard errors for such results but the parameters might still be reasonable for prediction applications. At least I’ve seen many output files from out competitors where the final gradient is still huge and I don’t think that has stopped the analysts from using the final estimates for prediction purposes.