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

[Simon Chamaillé-Jammes]

Hello all, 

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).

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.

best,

simon

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.

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...)

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