[R-sig-ME] meaning of the use.u argument in bootMer and simulateMerMod (Ben Bolker)

[Simon Chamaillé-Jammes]

I played around with Poisson-errors model, simulate() and the use.u
argument today, and when use.u is set to TRUE my confidence intervals
(here simply calculated as the interval between the 2.5 and 97.5
percentile of the bootstrapped statistics) sometimes did not include the
point estimate from the original data (this never happens with
use.u=F). 
So I would be reluctant to use this before understanding what's
happening.


> ------------------------------
> 
> Message: 5
> Date: Sat, 23 Jun 2012 20:24:01 +0000 (UTC)
> From: Ben Bolker <bbolker at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] meaning of the use.u argument in bootMer and
> 	simulateMerMod
> Message-ID: <loom.20120623T213124-231 at post.gmane.org>
> Content-Type: text/plain; charset=utf-8
> 
> Simon Chamaill?-Jammes <s.chamaille at ...> writes:
> 
> > recently I've been doing parametric boostrapping of glmer models (using
> > simulate/refit) to get confidence intervals for both fixed effect and
> > variance estimates. 
> > 
> > Yesterday I updated to the latest lme4 release on rforge, and discovered
> > (did I overlook it before?) the use.u argument in the bootMer function
> > (which is actually used in the simulateMerMod function called by
> > bootMer).
> 
>   I *think* it's been there for some time, and you just haven't
> noticed it?
> > 
> > in bootMer the definition of the use.u argument (default is FALSE) is:
> > 
> > logical, indicating, if the spherized random effects should be
> > simulated / bootstrapped as well. If FALSE, they are not changed, and
> > all inference is conditional on these.
> > 
> > in simulateMerMod its definition (default is FALSE) is:
> > 
> > (logical) generate new random-effects values (FALSE) or generate a
> > simulation condition on the current random-effects estimates (TRUE)?
> > 
> > Despite these explanations I don't quite get what's actually done and
> > I'm feeling uneasy about what should be done when bootstrapping to
> > produce confidence intervals for variance estimates. So far I have
> > implicitly used the default use.u = FALSE. All examples found on various
> > discussion/help lists seem to happily avoid this issue, maybe rightly I
> > don't know.
> > 
> > Any clarification, and answer on am I doing it right or not, would be
> > welcome.
> 
>   There's definitely some confusion and inconsistency between the
> documentation in both places (?bootMer and ?simulate.merMod); rather
> than charging in and fixing everything immediately, I will have to
> have some conversation with the primary developers to make sure that
> I've got everything right ...
> 
>   Bottom line: you're not being dumb, there are some messy/wrong
> bits in there.  For now, look carefully at the *code* to see what's
> going on.
> 
>   I would be inclined to do _unconditional_ simulations (i.e. 
> resampling the random effects); I don't, however, know offhand
> of any good references on the implications of doing it one way
> or the other (obviously the conditional simulations will give
> less variability/less conservative estimates of confidence intervals).
> 
> 
> 
> > PS1/ If one use bootMer to perform semi-parametric bootstrapping and
> > use.u is TRUE (the only option for now with semi-par. bootstrap), then
> > residuals are randomly permuted and added to the fitted values.
> 
>   Yes.
> 
> > PS2/ I believe what I'm asking here is related and similar to what was
> > asked here:
> > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q4/005049.html
> > which wasn't replied to (at least on the list).
> > 
> > PS3/ for my personal knowledge, I would be glad to get a hint on what
> > "spherized" random effects mean - google was of no help on this (which
> > somehow makes me feel good at times...)
> 
>   "Spherized" means a form of the random effects that are uncorrelated
> and where all observations have equal variance (so that the points
> would fall in a (hyper)sphere in multidimensional space. This is discussed
> under the rubric "spherical" in the introductory chapter of Doug
> Bates's book.
> 
> 
> > 
> > PS4/ for those interested, usage of use.u = TRUE with observation-level
> > random effect or nested random effects is problematic, ie. does not run:
> > you get things like: 
> > Error in dim(val) <- c(n, nsim) : 
> >   dims [product 164] do not match the length of object [123]
> > In addition: Warning message:
> > In etasim.fix + etasim.reff :
> >   longer object length is not a multiple of shorter object length
> 
>   Yes indeed (although I'm not 100% sure of the pattern --
> I think it may not require nested random effects to break). Will fix!
> 
>   thanks for your patience,
>     Ben Bolker
> 
> 
> 
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