Dear all,

I have the following mixed effects model with two crossed random effects
which I am using to model tree growth:

Model <- lmer (log(Growth) ~ log(Size) + log(Competition) + (1 + log(Size)
| Treatment) + (1 | Plot))

I am using this model to predict new data, and was glad to see that the
development version of lme4 now comes with a predict function that allows
both fixed and random effects to be used to generate predictions. However I
also need to be able to estimate the uncertainty in the predictions I am
making, which is a problem since predict () in lme4 doesn’t generate SE for
predictions.

I have tried to alternative approached. The first was to use simulate() to
generate a distribution of predicted values which I could then summarize
into uncertainty estimates. However, I found that the output of simulate()
is clearly different from that of predict(), regardless of how I treated
the use.u argument relating to random effects. When I take the mean
predicted value across 1000 or more simulations and compare it to the
output of predict(), it is clear that the two methods are producing
different results.

The second approach was to use the bootMer function as recommended in the
help file for predict(). From this I was able to obtain SE for the
parameter estimates. However, I’m not quite sure how to then translate
these into uncertainty in the predictions (i.e., how do I obtain SEs for
the predicted values?). Am I missing something obvious?

Any help/advice on the two approaches I have used would be much
appreciated, as would any suggestions on alternatives I have not
considered!

Best wishes,

Tommaso

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