In cboettig/nonparametric-bayes: Nonparametric Bayes inference for ecological models

Generate data under Beverton Holt model

require(pdgControl)
require(nonparametricbayes)
require(ggplot2)

Beverton-Holt function

Simulate some training data under a stochastic growth function with standard parameterization,

``` {r stateeqn} f <- BevHolt p <- c(1.5,.05) K <- (p[1]-1)/p[2]

Parameter definitions

```r
x_grid = seq(0, 1.5 * K, length=101)
T <- 40
sigma_g <- 0.1
x <- numeric(T)
x[1] <- 1

Noise function, profit function

z_g <- function() rlnorm(1, 0, sigma_g) #1+(2*runif(1, 0,  1)-1)*sigma_g #
profit <- profit_harvest(1,0,0)

Simulation

set.seed(111)
for(t in 1:(T-1))
  x[t+1] = z_g() * f(x[t], h=0, p=p)

Predict the function over the target grid (lag-1)

obs <- data.frame(x=x[1:(T-1)],y=x[2:T])
X <- x_grid

cboettig/nonparametric-bayes documentation built on May 13, 2019, 2:09 p.m.