We have fox trapping data. We have 12 grids of 40 traps each, but due to safety concerns, grid #7 and 7 traps from grid #4 were not sampled. Grids are sampled on 4 consecutive days. We plan to use the Huggins Closed Capture Model because we want to include 3 covariates in the model. Estimating abundance is of primary interest.
I am wondering how to best handle the missing data.
I plan to run 2 analyses using different grouping factors:
1) By Grid
· Here, I can enter the 12 grids as groups and constrain p=c=0 for grid 7. To do this, do I need to have one row in the encounter history file specifying grid 7? Something like this – where I have 4 dots representing the 4 occasions of missing data, a code for grid 7, and 3 dots for the missing values of the covariates?
.... 0 0 0 0 0 0 1 0 0 0 0 0 . . . ;
· How can I deal with the missing data on 7 traps of grid 4 since grids (not traps) are the groups?
We would like our grid results to be comparable, but having 7 fewer traps being sampled on one grid creates a problem. Is there a way to achieve comparable grid results?
2) By Habitat
· There are 4 habitat types, with (usually) 3 grids contained in each. But since grid 7 was not sampled, there are only 2 grids sampled in one of habitats. Should I simply not enter anything in the encounter history file for grid 7?
My habitat results are also incomparable, due to a difference in sample size and therefore precision. Is there a way to achieve comparable habitat results?
· How can I deal with the missing data on 7 traps of grid 4 since habitats (not traps or grids) are the groups?
Your thoughts are greatly appreciated!