playRN {AHMbook} | R Documentation |
Function generates replicated count data under the binomial N-mixture model of Royle (2004), then 'degrades' the counts to detection/nondetection and fits the Royle-Nichols (RN) model (Royle & Nichols 2003) using unmarked and estimates site-specific abundance.
playRN(M = 267, J = 3, mean.abundance = 1, mean.detection = 0.3, show.plots = TRUE, verbose = TRUE)
M |
The number of sites. |
J |
The number of visits to each site. |
mean.abundance |
Expected abundance at each site. |
mean.detection |
Expected detection at each survey at each site. |
show.plots |
choose whether to show plots or not. Set to FALSE when using function in simulations. |
verbose |
if FALSE, output to the console will be suppressed. |
A list with the following elements:
nsites |
The number of sites, equal to |
nvisits |
The number of visits, equal to |
coef |
A named vector of coefficients for the linear models for expected number and detection probability |
slope |
Slope of the regression of the estimated number on the true number; 1 if the model is perfect |
Marc Kéry & Andy Royle
Royle, J.A. & Nichols, J.D. (2003) Estimating abundance from repeated presence-absence data or point counts, Ecology, 84, 777-790.
Royle, J.A. (2004) N-mixture models for estimating population size from spatially replicated counts, Biometrics, 60, 108-115.
Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 6.13.1.
# Run a simulation with the default arguments and look at the results: playRN() # Execute the function using various settings playRN(M = 100, J = 3, mean.abundance = 0.1) # Increasing abundance playRN(M = 100, J = 3, mean.abundance = 1) playRN(M = 100, J = 3, mean.abundance = 5) playRN(M = 100, J = 3, mean.detection = 0.3) # Increasing detection playRN(M = 100, J = 3, mean.detection = 0.5) playRN(M = 100, J = 3, mean.detection = 0.7) playRN(M = 100, J = 20) # More visits playRN(M = 1000, J = 3) # More sites