I have a 100x100 km study area and I am using a mask spacing of 1000 m. This is my code:
regionsmall<-cbind(x=c(0, 100000, 100000, 0), y=c(0,0,100000,100000)) #10,000 km2
scen.6.0015.1000.1<-make.scenarios(noccasions=6, D=0.0015, sigma=1000, g0=0.1)
cluster3x3.2000<-make.grid(nx=3, ny=3, spacing=2000, detector="proximity")
trap3x3.2000<-make.systematic (cluster= cluster3x3.2000, spacing=16000, region = regionsmall)
mask3x3.2000<-make.mask(trap3x3.2000, buffer=7500, spacing=1000)
sim2000.3x3.occ6.100<-run.scenarios(nrepl = 10, scenarios = scen.6.0015.1000.1, seed=5,
trapset = trap3x3.2000, maskset = mask3x3.2000, fit = FALSE)
The above is for a 3x3 cluster with traps 2000 m apart and clusters 16000 m apart, on center.
With fit=FALSE n=130 animals, ndet=173, nmov=22 and dpa=1.17. So, this simulation is for pretty sparse data with relatively few multiple detections, which is pretty typical of the data I am simulating.
Standard errors of the estimates are pretty good (about 15%) it is just that the bias seems pretty unstable. Since the traps are spaced 2000 m apart, do you think that a mesh spacing of 1000 m is fine enough? I could increase the state space (say 200000 x 200000 m) to increase the number of animals, but that is going to slow things down again.
I will look at trying the nrepeats option. How would you recommend estimating the SE for density for several clusters? Thanks again.