Dear Ben and Steve,

Thanks for the advice on how to quantify uncertainty in predictions from
lmer. I have a couple of follow up questions/observations I'm hoping you
could help with.

1. I had misunderstood the use of the FUN argument in bootMer(). Specifying
"predict" did produce what I was after (i.e., SE for predictions). However,
I'm unsure how to go about using bootMer to get SE for predictions made on
new data (i.e., not just to obtain SE for the fitted values). I tried using
an approach similar to BUGS in which new data for which predictions are
wanted is simply added ad the end of the data frame and set to NA. This
worked for predict, which gave me estimates for these new data points. But
booMer seems to have ignored the additional data points and only provided
SEs for the data used to fit the model. Any suggestions? Again, I apologize
if the question is trivial!

2. I tried the simulate functions again and the mean of the simulations
does seem to converge better with predict. However, there are still clear
differences, which seem to be linked to how the random effects are treated.
I thought I would mention this in case you weren't aware of it.

Thanks again for your help,

Best wishes,

Tommaso

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