Hello,
Is there a procedure that I could use to validate/evaluate a 'secr' model's fit to the data? I have included two spatial covariates in my model and thus a beta and associated error are evaluated for these relationships, as in other linear models. Like a GLM, is there a measure such as R^2 that I can use to test how well my model is fitting the observed data?
I see previous posts (2010 and 2011) regarding goodness of fit procedures using sim.secr, and the potential issues with these approaches. But, unlike what I'm looking for, I believe these folks are looking for measures of overdispersion such as the C-hat used in MARK where the user is testing for differences from C=1.
Cheers,
Clayton