I'm looking for some clarification with pradel models (Pradrec) where lambda is a derived parameter. I should add that I'm actually doing variance components and modeling in RMark.
After rereading chapter 13 of the MARK book, I'd come to the conclusion that variance components does not calculate the correct value for mean lambda. Variance components uses the arithmetic mean and I want the geometric mean so as to not overestimate lambda. This requires doing the calculations by hand.
I tried to follow the chapter 13 examples with my dataset. Now when I compare different ways of calculating overall mean lambda from the lambda values and log(lambda) values (vcv versus geometric mean) all the mean values are different but all fall within a range of 0.0087. This seems trivial and nothing to worry about.
So does the variance components actually calculate mean lambda correctly using geometric means? Or should I still calculate geometric means and variances by hand for lambda in Pradel models?