Correlated detections- numerical convergence error

[darryl]

Hi Aakash
Check out the FAQ for some suggestions that might be useful viewtopic.php?f=40&t=665
Cheers
Darryl

[darryl]

It does sound like you're trying to include too many covariates so the model is overfitting the data and some parameters may not be fully estimable (indicated by the negative SEs).

With that sample size I think you don't want to be estimating any more than 2-3 occupancy-related parameters (how many covariates that translates to depends whether you have continuous or categorical valued covariates), and maybe 3-4 detection-related parameters.

From looking at the data, does it look like you have correlated detections (ie you have pulses of consecutive 1's and consecutive 0's)? If not, you can also try the regular single season model which has a simpler detection process.

[jhines]

Dear Aakash,

In general, Darryl's suggestion of a max of 2-3 covariates for occupancy and 3-4 for detection covariates in a model makes perfect sense. In practice, the max number of covariates will also depend on the data.

From the sample of data you included in your post, it appears that nearly all sites have detections. If the rest of the sites are like this, then naïve occupancy will be nearly 100%. When occupancy is very high, there isn't much possibility for occupancy to vary in the presence of a covariate. If occupancy is 100%, then the standard error of psi will be undefined and you will likely see error or warning messages about convergence and/or problems with the variance-covariance matrix.

Also, the correlated detections model adds new parameters to the standard model, which may reduce the number of covariate-effect parameters you can estimate. I recommend that you start with the simplest model (no covariates) and try adding covariates one by one until you see problems.

Cheers,

Jim