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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