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

[Ben Bolker]

  Are the frequentist methods *not* faster?  I'd be pretty surprised,
unless some you're hitting some terrible memory bottleneck or something.


On 2018-08-23 03:30 PM, Adam Mills-Campisi wrote:
> We originally tried to use stan to estimate the model, we were getting
> performance issues. I assumed that the frequentist approaches would be
> faster.
> 
> On Thu, Aug 23, 2018 at 12:28 PM Doran, Harold <HDoran using air.org> wrote:
> 
>> No. You can change to an improved BLAS or I have found the Microsoft R has
>> some built in multithreading that is fast for matrix algebra and it passes
>> that benefit to lmer. From some experience, you can improve computational
>> time of an lmer model with Microsoft R
>>
>> -----Original Message-----
>> From: R-sig-mixed-models <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
>> 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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