How to fix psi value by categorical covariate

posts related to the RPresence library, which may not be of general interest to users of 'classic' PRESENCE.

How to fix psi value by categorical covariate

Postby mrrose » Wed Aug 19, 2020 11:37 am

I cannot figure out how to fix psi values based on sites/units, or site/unit categorical covariates. The only example of fixing values I could find was for detection (in the genpres example).

I am trying to use habitat type as a occupancy covariate to investigate the effects of habitat type on several species. I have 3 types of habitat, including the baseline/intercept (factors a1, a2, and a3). The focal species was not found in a2 (the "forest" type), and as a result the beta estimate and SE for this factor have extremely large values (psi.psi.typeforest=-28.176516; SE=7.351706e+05).

the Design Matrix for psi (model$dmat$psi) is as follows:
a1 a2 a3
psi "1" "psi.typeforest" "psi.typeriparian"

I have tried using several ways to construct a data frame to use as the "fixed" data, but each time have gotten the message:
"fixed names must be in psi p1 p2 p3 p4 p5".

(There are 18 sites with 5 nights-sites 2,5,8,11,14,17 are forest type.)

Some of the attempts also triggered the following warning:
Error in fv[fixed$idx] <- fixed$value :
NAs are not allowed in subscripted assignments

What would the proper way to create a "fixed" data frame for this?

EDIT: After a second examination of my models I have found that some of the problem ones have potentially confounded variables/too many covariates for my sample size, and I will be constraining my model set to eliminate the issue.

However, I will leave the question up in hopes that it gets answered and an example/explanation of fixing psi parameters becomes available for RPresence here.
Last edited by mrrose on Wed Aug 19, 2020 3:11 pm, edited 1 time in total.
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