[R-sig-ME] Running

[Ben Bolker]

  I will give this some thought when I get a chance (hopefully someone
else will give it some thought and find some answers sooner ...)  In the
meantime -- do you really need parametric bootstrapping/bootMer to get
the confidence intervals you want?  It's quite possible that a simpler
approximation (e.g. assuming that the variation caused by uncertainty in
the top-level random-effects parameters is small relative to other
sources of variability) is adequate, given that you have thousands of
samples ...

On 2018-11-09 4:15 p.m., Jonathan Miller wrote:
> Dr. Bolker,
> 
> I am a Phd student at NCSU and struggling with a coding issue. I am
> bootstrapping some glmm model predictions in order to determine the
> uncertainty associated with their fixed effects.  I read your comments on
> https://github.com/lme4/lme4/issues/388 and have used a code similar to
> yours below (b3):
> 
> ## param, RE, and conditional
> b1 <- bootMer(fm1,FUN=sfun1,nsim=100,seed=101)
> ## param and RE (no conditional)
> b2 <- bootMer(fm1,FUN=sfun2,nsim=100,seed=101)
> ## param only
> b3 <- bootMer(fm1,FUN=function(x) predict(x,newdata=test,re.form=~0),
>               ## re.form=~0 is equivalent to use.u=FALSE
>               nsim=100,seed=101)
> 
> 
> It has worked well for me but takes an extremely long time to run. I am
> predicting 6 different wq indicators for 1,423 sites and the datasets range
> in size from 3,000 to 25,000 entries each.  The small one is relatively
> runs relatively ok, but the others are extremely slow. In my code (below),
> I also want to make more than one prediction (current conditions, possible
> future conditions) using the bootstrapping. Using "snow" in parallel
> doesn't seem to speed things up.  I thought of two possibilities, but am
> unsure how to implement them.
> 
> for (s in 1:1423){
> 
> bi <- bootMer(BI.mod,FUN=function(x)
> predict(x,newdata=pred.sites[s,],re.form=~0,REML=TRUE),
>               parallel="snow",nsim=1000,seed=101)
> bi.5 <- bootMer(BI.mod,FUN=function(x)
> predict(x,newdata=pred.sites.m5[s,],re.form=~0,REML=TRUE),
>               parallel="snow",nsim=1000,seed=101)
> }
> 
> 1) Can I predict the bootstrapped model using two different datasets at
> once to speed things up (i.e., pred.sites and pred.sites.m5)?
> 2) Can I use parallel processing of the initial loop (1,423 sites) outside
> of bootmer (perhaps with doParallel and foreach) and then run bootmer
> within that loop?  Though I have used foreach before, I find it hard to
> compile the data that I really want on the backend.
> 
> Thank you for your time and any suggestions you might have.
> 
> Sincerely,
> 
> Jonathan
> 
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