proximity detector specs

[jlaufenb]

This is a quick question for Murray that I decided to post here for archiving purposes. I am working with a hair snare dataset using 'proximity' detectors. However, the dataset does contain some capture histories where an animal was detected >1 time at a single site during a single occasion which suggests using a count-based detection model rather than a binary-based detection model. The occurence of these "multiple detections" is not frequent enough (in my opinion) to justify using a 'count' detector. My question is if I specify 'proximity' as the detector type in my call to the read.capthist function, will secr automatically (and more importantly, correctly) collapse those "multiple detections" into occasion-site specific binary detections or do I need to sift through the dataset and remove the redundant occasion-site specific detections?

Thanks
Jared

[murray.efford]

I believe secr.fit handles these appropriately (i.e. any number of detections will be treated as a '1' rather than a '0', though of course you have to override the data check with verify = FALSE). If in doubt, use reduce.capthist() like this to make the conversion:

Code: Select all

 myproxCH <- reduce(mycountCH, output = 'proximity')

Murray

[jlaufenb]

Thanks Murray. That was very helpful. I have another question. For example, say I wanted to model annual density for a 3 year/2 group dataset as 'a,a,a,b,b,b' and model g0 as 'a,b,a,b,a,b' when the data is structured as 6 sessions. How can I specify unique session covariate arguments for different model parameters? Can the sessioncov argument be a list of factor vectors and then use specific elements from that list in the model statements for different parameters?

Thanks again
Jared

[murray.efford]

Hi Jared
On the help page for secr.fit, sessioncov is described as an 'optional dataframe of values of session-specific covariate(s)'. A dataframe is a special sort of list and, yes, you can include any number of different factors or other predictors in sessioncov and use any subset in the formula for any parameter.
Murray