standard errors for Phi equal zero in multistate model

I am using multistate models set up via RMark to study perennial plants. I am modeling the effects of an experimental treatment on life history transitions as well as the effects of herbivory, a binary time-varying individual covariate. The models seems to work with either the treatment effect OR the herbivory covariate, but including both in the model frequently gives me standard errors that equal zero. This occurs for both additive (treatment + herbivory) and multiplicative models (treatment x herbivory) with some variation on how I specify the model in RMark.
My focal transitions are coded "N" and "D," with "D" being a hidden state; I am therefore applying treatment and covariate effects to the term in my model [N:toD]. My understanding from general R use is that
Are there any inherent issues with having multiple factors affect a transition within multistate model or is this likely an estimability issue related to my dataset? Ideally I'd like to build a single model that compares the effects of ambient levels of herbivory (which is fairly common) with our experimental treatment.
Some more details:
-As noted above, "D" is a hidden state with p = 0. Above-ground states ("Y" and "N") have p = 1.
-Because I am interested in the dynamics of the hidden state I constrain survival to be constant over time.
-N is about 200 individuals.
Treatment x Herbivory effect, version 1
My focal transitions are coded "N" and "D," with "D" being a hidden state; I am therefore applying treatment and covariate effects to the term in my model [N:toD]. My understanding from general R use is that
- Code: Select all
N:toD:time:treatment:eaten
- Code: Select all
N:toD:time + N:toD:time:eaten + N:toD:time:treatment + N:toD:time:eaten:treatment
Are there any inherent issues with having multiple factors affect a transition within multistate model or is this likely an estimability issue related to my dataset? Ideally I'd like to build a single model that compares the effects of ambient levels of herbivory (which is fairly common) with our experimental treatment.
Some more details:
-As noted above, "D" is a hidden state with p = 0. Above-ground states ("Y" and "N") have p = 1.
-Because I am interested in the dynamics of the hidden state I constrain survival to be constant over time.
-N is about 200 individuals.
Treatment x Herbivory effect, version 1
- Code: Select all
Psi.1 <- list(formula = ~-1 +
N:toY:time +
N:toD:time:treatment:eaten +
Y:toY:time +
Y:toN:time +
D:toN:time +
D:toD:time)
Treatment x Herbivory effect, version 1
Psi.2 <- list(formula = ~-1 + #Remove intercept (?)
N:toY:time +
N:toD:time + N:toD:time:eaten + N:toD:time:treatment + N:toD:time:eaten:treatment +
Y:toY:time +
Y:toN:time +
D:toN:time +
D:toD:time)