[R-sig-ME] Running

[Ben Bolker]

  [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 using gmail.com
> <mailto:bbolker using 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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