[R-sig-ME] How to use all the cores while running glmer on a piecewise exponential survival with

[Doran, Harold]

Running the model on multiple cores won’t work because lmer isn’t written that way. One idea I’ve toyed with is start with a small-ish sample and get results. Plug those in as starting values to your next run which uses larger sample, but takes fewer steps because you’re closer to the max. Repeat until the difference in the param estimates from prior run is less than some tolerance.


From: Adam Mills-Campisi <adammillscampisi using gmail.com>
Sent: Thursday, August 23, 2018 3:25 PM
To: Doran, Harold <HDoran using air.org>
Cc: r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] How to use all the cores while running glmer on a piecewise exponential survival with

That's the plan, the real question is how big should the samples be. The faster we can estimate the model, the bigger the sample can be. If I can run the model on multiple cores that would significantly increase the sample size.

On Thu, Aug 23, 2018 at 12:23 PM Doran, Harold <HDoran using air.org<mailto:HDoran using air.org>> wrote:
One idea, though, is you can take samples from your very large data set and estimate models on the samples very quickly.

-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org<mailto:r-sig-mixed-models-bounces using r-project.org>> On Behalf Of Adam Mills-Campisi
Sent: Thursday, August 23, 2018 3:18 PM
To: r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>
Subject: [R-sig-ME] How to use all the cores while running glmer on a piecewise exponential survival with

I am estimating a piecewise exponential, mixed-effects, survival model with recurrent events. Each individual in the dataset gets an individual interpret (where using a PWP approach). Our full dataset has 10 million individuals, with 180 million events. I am not sure that there is any framework which can accommodate data at that size, so we are going to sample. Our final sample size largely depends on how quickly we can estimate the model, which brings me to my question: Is there a way to mutli-thread/core the model? I tried to find some kind of instruction on the web and the best lead I could find was a reference to this list serve.
Any help would be greatly appreciated.

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