Hi all,
I am working on single-species single-season occupancy models where I have camera trap data from over 300 sites with 27 re-samples (24 hr period = sample). However, I am getting monster c-hat values for my global model when I assess model fit.
I had this issue earlier and realized that detection histories with many 1's seemed to be throwing my goodness of fit testing. Guessing this may have been a trap-happiness response that my covariates were not accounting for, I divided my detection histories into 3 day survey periods and got acceptable c-hat estimates. However, I ran into some later convergence issues I thought using my full detection histories might help resolve and am now trying to find a way to use all 27 re-samples.
I created a series of Markov-dependency/memory covariates to represent trap-response where the value=0 previous to a detection and then =1 following a detection for x # of days (up to all days following detection). Models with these covariates always performed better than models without them. Unfortunately, my c hat estimates are still enormous.
Does anyone have any suggestions as to what could be going on and what I need to do to move forward? I am pretty certain my covariates account for most variation and that trap-response is my issue given the c-hat values near 1 when I used 3 day samples.
Thanks!!
-Woefully-stuck grad student