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Message-ID: <AM6PR0402MB333313B9252D5B1FD9827949E8560@AM6PR0402MB3333.eurprd04.prod.outlook.com>
Date: 2019-12-15T21:08:52Z
From: Dimitris Rizopoulos
Subject: Is there a way to deal with errors such as this?
In-Reply-To: <c5289090-f17c-c6f9-76f2-bc78e011fc78@auckland.ac.nz>

In the datasets for which glmer() fails you could try fitting the model with GLMMadaptive or glmmTMB. The model you?re fitting is always the same, the optimization and numerical integration algorithms change in the different packages.

Moreover, if the main aim of the simulation is to assess some properties of the model and not of the optimization algorithm, you could help the optimization procedure by supplying as starting values for the model parameters the true parameters values from which you simulate the data.

Best,
Dimitris

?-
Dimitris Rizopoulos
Professor of Biostatistics
Erasmus University Medical Center
The Netherlands

________________________________
???: ? ??????? R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> ?? ?????? ??? ?????? Rolf Turner <r.turner at auckland.ac.nz>
????????: ???????, ?????????? 15, 2019 21:45
????: Daniel L?decke
????.: r-sig-mixed-models at r-project.org
????: Re: [R-sig-ME] Is there a way to deal with errors such as this?


On 15/12/19 11:20 pm, Daniel L?decke wrote:

> The model seems to fit w/o error when you use "glmmTMB". Unlike
> glmmADAPTIVE, which uses a more time consuming adaptive Gaussian
> quadrature rule, glmmTMB might be faster to run the models (even faster
> than glmer(), probably).

<SNIP>

Thanks for this advice. For some reason I had it my head that
GLMMadaptive worked better than glmmTMB. I don't know why I got that
idea. I will do some experimentation and timing assessments later on.
But for the moment I'm sticking with glmer() and the strategy of
discarding the simulated data set and generating a new one if glmer()
throws an error.

Thanks again.

cheers,

Rolf

--
Honorary Research Fellow
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

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