simPOP {AHMbook} | R Documentation |
Simulate data for a demographic state-space model
Description
Simulate multiple time-series of counts under a pure Markov model (with exponential population model) or under an extended Markov model with exponential-plus-random-immigration population model; see Sollmann et al.(2015). Default is Markov model, setting sd.rho
to a value greater than 0 changes to extended Markov and sets the amount of random immigration.
Usage
simPOP(M = 100, T = 10, mean.lam = 3, beta.lam = 0, sd.log.lam = 0,
mean.gamma = 1.0, beta.gamma = 0, sd.log.gamma.site = 0,
sd.log.gamma.time = 0, sd.log.gamma.survey = 0, sd.rho = 0,
mean.p = 0.6, beta.p = 0, sd.logit.p.site = 0, sd.logit.p.time = 0,
sd.logit.p.survey = 0, show.plot = TRUE)
Arguments
M |
The number of sites. |
T |
The number of years. |
mean.lam |
The mean abundance for year 1. |
beta.lam |
The covariate coefficient for lambda. |
sd.log.lam |
The over-dispersion in lambda. |
mean.gamma |
The mean population growth rate. |
beta.gamma |
The covariate coefficient for gamma. |
sd.log.gamma.site |
SD of random site effects for gamma. |
sd.log.gamma.time |
SD of random time effects for gamma. |
sd.log.gamma.survey |
SD of random survey (site+time) effects for gamma. |
sd.rho |
The random immigration term. |
mean.p |
The mean detection probability. |
beta.p |
The covariate coefficient for p. |
sd.logit.p.site |
SD of random site effects for p on the logit scale. |
sd.logit.p.time |
SD of random time effects for p on the logit scale. |
sd.logit.p.survey |
SD of random survey (site+time) effects for p on the logit scale. |
show.plot |
Choose whether to show plots or not. Set to FALSE when using function in simulations. |
Value
A list with the values of the arguments entered and the following additional elements:
Xsite1 |
M vector, site covariate affecting initial abundance (lambda). |
Xsiteyear1 |
M x T matrix, yearly site covariate affecting recruitment (gamma). |
Xsiteyear2 |
M x T matrix, yearly site covariate affecting detection (p). |
eps.N |
M vector, site over-dispersion at t = 1. |
lambda |
M vector, abundance in year 1. |
eps.gamma.site |
M vector, random site effect for gamma. |
eps.gamma.time |
T vector, random time effect for gamma. |
eps.gamma.survey |
M x T matrix, random survey effect for gamma. |
gamma |
M x T matrix, population growth rate. |
rho |
(T-1) vector, immigration rate. |
eps.p.site |
M vector, random site effect for detection. |
eps.p.time |
T vector, random time effect for detection. |
eps.p.survey |
M x T matrix, random survey effect for detection. |
p |
M x T matrix, detection probability. |
N |
M x T matrix, true population. |
C |
M x T matrix, simulated counts. |
zeroNyears |
scalar, sum(N == 0). |
Nextinct |
scalar, number of sites where N == 0 at time T. |
extrate |
scalar, proportion of sites where N == 0 at time T. |
sumN |
T vector, total population in each year. |
gammaX |
(T-1) vector, realized population growth rate. |
Author(s)
Marc Kéry & Andy Royle
References
Sollmann, R. et al. (2015) An open-population hierarchical distance sampling model. Ecology, 96, 325-331.
Kéry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 1.7.1.
Examples
# Run with the default arguments and look at the structure of the output
set.seed(123)
tmp <- simPOP()
str(tmp)
head(tmp$C)