Poisson2_1D: 1-Dimensional NonHomogeneous Poisson example.

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

1-Dimensional NonHomogeneous Poisson example.

Description

Point data and count data, together with intensity function and expected counts for a unimodal nonhomogeneous 1-dimensional Poisson process example.

Usage

data(Poisson2_1D)

Format

The data contain the following R objects:

lambda2_1D:

A function defining the intensity function of a nonhomogeneous Poisson process. Note that this function is only defined on the interval (0,55).

cov2_1D:

A function that gives what we will call a 'habitat suitability' covariate in 1D space.

E_nc2

The expected counts of the gridded data.

pts2

The locations of the observed points (a data frame with one column, named x).

countdata2

A data frame with three columns, containing the count data:

x

The grid cell midpoint.

count

The number of detections in the cell.

exposure

The width of the cell.

Examples

if (require("ggplot2", quietly = TRUE)) {
  data(Poisson2_1D)
  p1 <- ggplot(countdata2) +
    geom_point(data = countdata2, aes(x = x, y = count), col = "blue") +
    ylim(0, max(countdata2$count, E_nc2)) +
    geom_point(
      data = countdata2, aes(x = x), y = 0, shape = "+",
      col = "blue", cex = 4
    ) +
    geom_point(
      data = data.frame(x = countdata2$x, y = E_nc2), aes(x = x),
      y = E_nc2, shape = "_", cex = 5
    ) +
    xlab(expression(bold(s))) +
    ylab("count")
  ss <- seq(0, 55, length.out = 200)
  lambda <- lambda2_1D(ss)
  p2 <- ggplot() +
    geom_line(
      data = data.frame(x = ss, y = lambda),
      aes(x = x, y = y), col = "blue"
    ) +
    ylim(0, max(lambda)) +
    geom_point(data = pts2, aes(x = x), y = 0.2, shape = "|", cex = 4) +
    xlab(expression(bold(s))) +
    ylab(expression(lambda(bold(s))))
  multiplot(p1, p2, cols = 1)
}

inlabru documentation built on Aug. 9, 2025, 1:08 a.m.