[please keep r-sig-mixed-models in the Cc: if possible - although I see it's a judgment call in this case because the e-mail contains both generally pertinent info (uncertainty of FE small) and a personal-ish message ...] Just to be clear, (1) I was suggesting that the uncertainty of the fixed effects might be *large* with respect to the uncertainty of the random effects, and largely independent of it; (2) have you already tried implementing other (approximate, faster) methods for the uncertainty on a small subset of the sites to convince yourself that you really need the full PB method?
On 2018-11-09 6:28 p.m., Jonathan Miller wrote:
Thank you.? You are right the uncertainty of the fixed effects is
smaller than the others, but is of importance for my project. I
appreciate any thoughts you have when you have time to get to it.
Jonathan
On Fri, Nov 9, 2018, 5:17 PM Ben Bolker <bbolker at gmail.com
<mailto:bbolker at gmail.com>> wrote:
? 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
>
>? ? ? ?[[alternative HTML version deleted]]
>
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