Russ,
You understood correctly. Thanks for the code descriptions. At first I
realized that the true predictions {(pred<-pred  <-  sum(summ *
fixef(glmmadmb_object))} had to be transformed on the log link. I could not
get exp(pred) to work (Inf result). My work around was

pred<-sum(exp(predict(glmmadmb_model, newdata)))

and then the last line of your code above worked and gave acceptable
results.

The results are very interesting. Since posting this question, I did go
through summing the confidence limits via my original idea, just to see
results. The memory required to perform mm%*%vcov(model)%*%t(mm) on a
matrix of 150000 was far too much for my machine, so I had to subset the
data and repeat the matrix multiplication step over and over..very tedious,
but I finished, merged the subsets back together, and summed the upper and
lower limits as well as the true predictions. Results were the same numbers
as via your method. The only difference was I didn't have to split the
dataset up, so much less effort. So, thanks again.

I would like have a little more understanding of the statistical basis for
this step:

summ <- apply(mm, 2, sum)

Could you explain (as much as you care to) about why this is statistically
valid? My understanding now is that it is the sum of the measures of each
variable in the 150000. I have coded categorical variables as well as
continuous variables in the model. I appreciate any information,
documentation, articles, that can help me understand why this works. I
refuse be that guy that just "ran the code", and I will probably need more
documentation/evidence that this has been done before in order to be
accepted for publication. After months of research, I still haven't found
the right papers to reference.
Thanks!
-Reuben

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