| issj.sim | R Documentation | 
Function to simulate open distance sampling data for the Island Scrub Jays, based on Sollmann et al (2015).
To recreate the data sets used in the book with R 3.6.0 or later, include sample.kind="Rounding" in the call to set.seed. This should only be used for reproduction of old results.
issj.sim(B, db, lam, sigma, phi, gamma, npoints, nyrs, nbsize = -1.02)
| B | Radius of the circle sampled; a site is a circle of radius B around a point. | 
| db | Distance categories; a vector of cut points from 0 to B inclusive. | 
| lam | Expected abundance per site, a vector of length  | 
| sigma | Scale parameter of the half-normal detection function at each site,  a vector of length  | 
| phi | Survival probability | 
| gamma | Recruitment rate | 
| npoints | Number of sites where point counts are conducted. | 
| nyrs | Number of years | 
| nbsize | Size parameter for the negative binomial distribution used to generate individual counts per site for year 1. | 
A list with the following elements:
| NcList  | A list with one element per year, with distances of all animals from the point. | 
| detList  | A list with one element per year, a  | 
| N  | The (true) number of animals at each point for each year, a  | 
| cell  | The site IDs where point counts are conducted. | 
| y  | 
 | 
| dclass  | a vector with the distance class for each animal detected | 
| site  | a corresponding vector with the site for each animal detected | 
| nsite  | the number of sites in the study area | 
| lam, phi, gamma, sigma  | the values of the corresponding arguments | 
Marc Kéry & Andy Royle, based on Sollmann et al (2015)
Sollmann, R., Gardner, B., Chandler, R.B., Royle, J.A., Sillett, T.S. (2015) An open population hierarchical distance sampling model. Ecology 96, 325-331.
Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 9.7.1.
# A toy example with just 20 sites
set.seed(2015)
tmp <- issj.sim(B = 300,
    db = c(0,50, 100, 150, 200, 250, 300),
    lam = c(3.01, 7.42, 20.51, 1.60, 0.42, 3.42, 8.24, 0.66, 0.32, 0.39, 0.46, 0.52,
      0.63, 0.36, 4.93, 0.47, 2.07, 0.42, 0.48, 0.47),
    sigma = c(110, 91, 70, 114, 135, 101, 88, 130, 133, 134, 134, 135, 131, 135, 100,
      135, 110, 135, 134, 135),
    phi = 0.6, gamma = 0.35,
    npoints = 15, nyrs = 4)
str(tmp)
# Compare the number detected with the true numbers present
with(tmp, cbind(y, N[cell, ]))
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