inst/examples/spde.posterior.R

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

\donttest{
if (bru_safe_inla() && require(ggplot2, quietly = TRUE)) {

  # Load 1D Poisson process data

  data(Poisson2_1D, package = "inlabru")


  # Take a look at the point (and frequency) data

  ggplot(pts2) +
    geom_histogram(aes(x = x), binwidth = 55 / 20, boundary = 0, fill = NA, color = "black") +
    geom_point(aes(x), y = 0, pch = "|", cex = 4) +
    coord_fixed(ratio = 1)

  # Fit an LGCP model with  and SPDE component

  x <- seq(0, 55, length.out = 20)
  mesh1D <- fm_mesh_1d(x, boundary = "free")
  mdl <- x ~ spde1D(x, model = INLA::inla.spde2.matern(mesh1D)) + Intercept(1)
  fit <- lgcp(mdl, data = pts2, domain = list(x = mesh1D))

  # Calculate and plot the posterior range

  range <- spde.posterior(fit, "spde1D", "range")
  plot(range)

  # Calculate and plot the posterior log range

  lrange <- spde.posterior(fit, "spde1D", "log.range")
  plot(lrange)

  # Calculate and plot the posterior variance

  variance <- spde.posterior(fit, "spde1D", "variance")
  plot(variance)

  # Calculate and plot the posterior log variance

  lvariance <- spde.posterior(fit, "spde1D", "log.variance")
  plot(lvariance)

  # Calculate and plot the posterior Matern correlation

  matcor <- spde.posterior(fit, "spde1D", "matern.correlation")
  plot(matcor)

  # Calculate and plot the posterior Matern covariance

  matcov <- spde.posterior(fit, "spde1D", "matern.covariance")
  plot(matcov)
}
}