I'm working with an enclosed European-rabbit population, which means that there is no random emigration/immigration. However, in every capture occasion we release some rabbits to the exterior for the reinforcement of the wild rabbit population, which creates a bias in survival estimation (and it can be considered a controlled emigration :)). Of all captures/recaptures, 46% were released at some point. I have the information of all the individuals that were released, so, what I'm trying to do is to input the capture histories with a right-censoring (RC) column at the end where 1 means the rabbit remains in the enclosure and 2 means it was released. Then I created a vector (“last_capture”) that defines the last capture as the last time a capture history value is different from zero. And I created another vector that defines the following condition: if RC=2, then the last capture that counts for the model is last_capture; if RC=1 then individuals are analysed up to the last capture occasion. However, since I haven't found anything about anyone having done this before, I don't know if this type of models allow me to do it. I have been trying to run the model but it's constantly giving these errors:
[Note] Infinite values were detected in model variable: logProb_y. Compiling [Note] This may take a minute. [Note] Use 'showCompilerOutput = TRUE' to see C++ compilation details. running chain 1... warning: logProb of data node y[1, 1]: logProb is -Inf. warning: logProb of data node y[2, 1]: logProb is -Inf. warning: logProb of data node y[3, 1]: logProb is -Inf. warning: logProb of data node y[4, 1]: logProb is -Inf (etc).
I also removed from the data the rabbits that were only caught once (which also includes the rabbits that were only caught at the last capture occasion), but the errors persist. I don't know what to change anymore and I was hoping someone could give me an opinion about my conclusions and maybe enlighten me a bit :’)
Thank you so much in advance,
Kind regards,
Mariana Santos