ricker function

Ricker model with Poisson observations.

ricker is a pomp object encoding a stochastic Ricker model with Poisson measurement error. data

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

Returns

A pomp object containing the Ricker model and simulated data.

Details

The state process is c("$N[t+1] =\n$", "$r N[t] exp(-c N[t]+e[t])$"), 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 $Poisson(phi N[t])$.

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')

See Also

More examples provided with pomp: blowflies, childhood_disease_data, compartmental_models, dacca(), ebola, gompertz(), ou2(), pomp_examples, rw2(), verhulst()