ricker {pomp} | R Documentation |
Ricker model with Poisson observations.
Description
ricker
is a ‘pomp’ object encoding a stochastic Ricker model
with Poisson measurement error.
Usage
ricker(r = exp(3.8), sigma = 0.3, phi = 10, c = 1, N_0 = 7)
Arguments
r |
intrinsic growth rate |
sigma |
environmental process noise s.d. |
phi |
sampling rate |
c |
density dependence parameter |
N_0 |
initial condition |
Details
The state process is N_{t+1} = r N_{t} \exp(-c N_{t}+e_{t})
, where the e_t
are i.i.d. normal
random deviates with zero mean and variance \sigma^2
. The
observed variables y_t
are distributed as
\mathrm{Poisson}(\phi N_t)
.
Value
A ‘pomp’ object containing the Ricker model and simulated data.
See Also
More examples provided with pomp:
blowflies
,
childhood_disease_data
,
compartmental_models
,
dacca()
,
ebola
,
gompertz()
,
ou2()
,
pomp_examples
,
rw2()
,
verhulst()
Examples
po <- ricker()
plot(po)
coef(po)
simulate(po) |> plot()
# takes too long for R CMD check
## generate a bifurcation diagram for the Ricker map
p <- parmat(coef(ricker()),nrep=500)
p["r",] <- exp(seq(from=1.5,to=4,length=500))
trajectory(
ricker(),
times=seq(from=1000,to=2000,by=1),
params=p,
format="array"
) -> x
matplot(p["r",],x["N",,],pch='.',col='black',
xlab=expression(log(r)),ylab="N",log='x')
[Package pomp version 5.11.0.0 Index]