Hello,

I’ll try to make this more succinct.  I'm working with longitudinal data and having difficulty understanding the correct formulation for a partially crossed random effects model.

I'm evaluating 2 competing hypotheses.

1) marten use (detections/day) of high elevation habitat is seasonal
and/or
2) construction and wind farm operation affects marten use of high elevation habitat.

-These time periods overlap… i.e., construction activities occurred in winter and summer and vary in length (42 - 163 days).
-Marten come in and out of the study but need to be monitored for at least 2 seasons/3 construction periods.  Also, marten contribute differently depending on the amount of high elevation habitat within their home range.

So far my formula has been:

fm1 <- lmer(detections ~ localseason + constructionperiod2 +  (1|marten), data_std, family=poisson(log))

How would I change this to account for the partial crossing that occurred between marten and periods?  Would it look like this?

fm1 <- lmer(detections ~ localseason + constructionperiod2 + (1|localseason:marten) + (1|constructionperiod2:marten) + (1|marten), data_std, family=poisson(log))

Any help would be greatly appreciated.

Thanks,

Alexej


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