Hello Guys
I have a question and maybe it was already asked, but I don't know exactly how to formulate it. And so, I couldn't find any similar question.
I am working for my master thesis with Snow Voles (Chinomys nivals). I have a dataframe of captured individuals from the last 13 years, and sampling occasion were once in early summer and once in late summer. I am estimating summer and winter survival, and the estimates will be used in a Two-Season-Population-Projection-Matrix. I am working with a multistate model. I have different environmental covariates like mean temperature over the season, amount of days with a certain snow cover depth and so on.
My model looks like: S~(sex:age:season + environment + environment:season). Sex (M,F), age (J,A) and Season (summer,winter) are all binary variables. Environment is a numeric variable (most of the times).
My issue I have now is the fact, that I assume to have environmental covariates, that only affect the summer survival rate or only winter survival rate. For example the Mean temperature of June might only influence the summer survival rate, but has no/barley an effect on winter survival rate. The same might be for number of avalanches which seems to have an influence on winter survival rate but not on summer.
S~(sex:age:season + environment + environment:season)
In this model, I try to distinguish between the effect of the environment covariate on summer and on winter. But I wish to force it in a way, that this special environment has an interaction with only summer.
It should look like: S~(sex:age:season + environment + environment:Season(Summer))
But I have no idea how to describe it like that.
I hope somebody can help me.
Thanks,
Steffen