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Poisson3_1D

1-Dimensional NonHomogeneous Poisson example.

Description

Point data and count data, together with intensity function and expected counts for a multimodal nonhomogeneous 1-dimensional Poisson process example. Counts are given for two different gridded data interval widths.

Usage

data(Poisson3_1D)

Format

The data contain the following R objects:

lambda3_1D:: A function defining the intensity function of a nonhomogeneous Poisson process. Note that this function is only defined on the interval (0,55).
E_nc3a: The expected counts of gridded data for the wider bins (10 bins).
E_nc3b: The expected counts of gridded data for the wider bins (20 bins).
pts3: The locations of the observed points (a data frame with one column, named x).
countdata3a: A data frame with three columns, containing the count data for the 10-interval case:
countdata3b: A data frame with three columns, containing the count data for the 20-interval case:

x: The grid cell midpoint.
count: The number of detections in the cell.
exposure: The width of the cell.

Examples

library(ggplot2)
data(Poisson3_1D)
# first the plots for the 10-bin case:
p1a <- ggplot(countdata3a) +
  geom_point(data = countdata3a, aes(x = x, y = count), col = "blue") +
  ylim(0, max(countdata3a$count, E_nc3a)) +
  geom_point(
    data = countdata3a, aes(x = x), y = 0, shape = "+",
    col = "blue", cex = 4
  ) +
  geom_point(
    data = data.frame(x = countdata3a$x, y = E_nc3a),
    aes(x = x), y = E_nc3a, shape = "_", cex = 5
  ) +
  xlab(expression(bold(s))) +
  ylab("count")
ss <- seq(0, 55, length = 200)
lambda <- lambda3_1D(ss)
p2a <- ggplot() +
  geom_line(
    data = data.frame(x = ss, y = lambda), aes(x = x, y = y),
    col = "blue"
  ) +
  ylim(0, max(lambda)) +
  geom_point(data = pts3, aes(x = x), y = 0.2, shape = "|", cex = 4) +
  xlab(expression(bold(s))) +
  ylab(expression(lambda(bold(s))))
multiplot(p1a, p2a, cols = 1)

# Then the plots for the 20-bin case:
p1a <- ggplot(countdata3b) +
  geom_point(data = countdata3b, aes(x = x, y = count), col = "blue") +
  ylim(0, max(countdata3b$count, E_nc3b)) +
  geom_point(
    data = countdata3b, aes(x = x), y = 0, shape = "+",
    col = "blue", cex = 4
  ) +
  geom_point(
    data = data.frame(x = countdata3b$x, y = E_nc3b),
    aes(x = x), y = E_nc3b, shape = "_", cex = 5
  ) +
  xlab(expression(bold(s))) +
  ylab("count")
ss <- seq(0, 55, length = 200)
lambda <- lambda3_1D(ss)
p2a <- ggplot() +
  geom_line(
    data = data.frame(x = ss, y = lambda), aes(x = x, y = y),
    col = "blue"
  ) +
  ylim(0, max(lambda)) +
  geom_point(data = pts3, aes(x = x), y = 0.2, shape = "|", cex = 4) +
  xlab(expression(bold(s))) +
  ylab(expression(lambda(bold(s))))
multiplot(p1a, p2a, cols = 1)

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

v2.3.1

GPL (>= 2)

Authors

Finn Lindgren [aut, cre, cph] (<https://orcid.org/0000-0002-5833-2011>, Finn Lindgren continued development of the main code), Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code), David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for Gorilla data import and sampling, multiplot tool), Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling method), Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided MRSea data import code), Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes)

Initial release