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

[D. Rizopoulos]

I think the idea of how to efficiently implement Laplace and adaptive Gauss-Hermite integration in nested random effects designs for GLMMS is described in https://dx.doi.org/10.1198/106186006X96962



-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Doran, Harold
Sent: Friday, August 24, 2018 4:21 PM
To: Ben Bolker <bbolker using gmail.com>; 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

@ben, I like that idea. I've done that with some recent work that reduces some dimensionality in the integration and makes the problem easier to compute. I just don't know the current problem well enough to know if that is feasible here.

But, it's certainly an idea to explore.



-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Ben Bolker
Sent: Thursday, August 23, 2018 4:09 PM
To: 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


  Harold, what do you think of my suggestion (partition problem into multiple conditionally independent subsets, evaluate separate deviances on workers, run top-level optimization on a central 'master' processor)?
 Am I missing something (except that some problems can't easily be partitioned that way?)

  FWIW I think Doug Bates has pointed out in the past that for simple (e.g. nested, not crossed) designs, the whole problem can be reformulated in a more efficient way (of course I can't dig up that e-mail ...).  lme4's strength is that it can handle the complex cases, and so far no-one has had the time/energy/interest/capability of implementing any of Doug's "special case" strategies, at least in lme4
-- may be done elsewhere in R, or in Doug's MixedModels.jl ...

  cheers
   Ben Bolker


On 2018-08-23 03:32 PM, Doran, Harold wrote:
> 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-bo
> unces 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.o
> rg>
> 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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