Hello all,
I am working on a multistate analysis with 9 capture occasions and 2 geographical states ("T" or "E"). Each occasion was in one of 2 seasons: Winter or Spring. I am interested in investigating how season and size of the individual affected transition probabilities, but am having issues with transition probability estimates near 1.The estimates near 1 are E -> E from Spring to Winter and T -> T from Winter to Spring. Looking at the capture histories, from Spring to Winter there were a few instances of E -> E transitions and no instances of E -> T transitions. Looking from Winter to Spring, there were quite a few instances of T -> T and no instances of T -> E. So it makes sense that these transition probabilities are being estimated at 1. The issue I'm having is with obtaining accurate standard error/variance estimates.
If I do not consider the individual covariate of size, I can utilize the sin link. My variable in that case was defined as:
Psi.season = list(formula=~-1 + stratum:tostratum:season, link="sin")
Using that approach, I obtained reasonable standard errors and confidence intervals (and found that transition probabilities did vary significantly with state and season).
However, if I want to consider the individual covariate of size as well, I can only use the logit link and am left with standard errors and confidence intervals that cannot be estimated for the E -> E Spring and T -> T Winter parameters. My variable in that case was defined as:
Psi.season.size = list(formula=~-1 + stratum:tostratum:season + stratum:tostratum:size)
In the second case considering the individual covariate, I have tried simulating annealing (options="SIMAANEAL") and refitting with new starting values (retry = 1) but am still left without accurate standard errors for the two transition parameters near 1.
Are there any suggestions for obtaining better standard error estimates for those two parameters while considering the individual size variable?
Thank you!