Hello,
Background information of my study design. In my process.data function I have nocc = 75, model = "Nest", groups = c("Year","Site"). I have 2 sites: 1 site has 24 years of data and other has 10 years of data.
The best supported model is DSR ~ -1 + cinit + NestAge + Site:Year.
cinit - is the initiation date and is centered.
I followed the mallard example to extract the values of interest, where I want DSR of nest ages 1-24. I used the find.covariates function and assigned 1-24 for each site and each year. the reminaing 51 values are there.
Then I created a design matrix using fill.covariates, where I ended up having a very large matrix. To get my survival estimates, I used the compute.real function and it looks something like this
topmodel.survival <- compute.real(final_top_model, design = design)[c(1:24, 75:98, 149:172.... so on)]
I did this to get the DSR for each nest age for each site and year.
Now, to get the cumulative nest success, a nest will surivive the 24 day incubation period, it is easy enough to get that product for each year and site. The problem I am having is getting the sampling variance or se for that product. I have read the sections that covers delta method in the mark book and looked at other threads on this forum. I found them to be helpful, but I'm afraid of messing things up since for nest survival models can use nestage.
When I use the compute.real, do I need to exclude the [c(1:24).. ] part to get all of the 75 estimates for each year and set vcv = true? Will this allow me to have those values to use the deltamethod.special function? I ultimately want to get the 24 day nesting success with a se or 95% CI around those points for each site and year.
I will appreciate any guidance or help!!!