inlabru (version 2.11.1)

bincount: 1D LGCP bin count simulation and comparison with data

Description

A common procedure of analyzing the distribution of 1D points is to chose a binning and plot the data's histogram with respect to this binning. This function compares the counts that the histogram calculates to simulations from a 1D log Gaussian Cox process conditioned on the number of data samples. For each bin this results in a median number of counts as well as a confidence interval. If the LGCP is a plausible model for the observed points then most of the histogram counts (number of points within a bin) should be within the confidence intervals. Note that a proper comparison is a multiple testing problem which the function does not solve for you.

Usage

bincount(
  result,
  predictor,
  observations,
  breaks,
  nint = 20,
  probs = c(0.025, 0.5, 0.975),
  ...
)

Value

An data.frame with a ggplot attribute ggp

Arguments

result

A result object from a bru() or lgcp() call

predictor

A formula describing the prediction of a 1D LGCP via predict().

observations

A vector of observed values

breaks

A vector of bin boundaries

nint

Number of integration points per bin. Increase this if the bins are wide and

probs

numeric vector of probabilities with values in [0,1]

...

arguments passed on to predict.bru()

Examples

if (FALSE) {
if (require(ggplot2) && require(fmesher)) {
  # Load a point pattern
  data(Poisson2_1D)

  # 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
  x <- seq(0, 55, length.out = 50)
  mesh1D <- fm_mesh_1d(x, boundary = "free")
  mdl <- x ~ spde1D(x, model = inla.spde2.matern(mesh1D)) + Intercept(1)
  fit.spde <- lgcp(mdl, pts2, domain = list(x = c(0, 55)))

  # Calculate bin statistics
  bc <- bincount(
    result = fit.spde,
    observations = pts2,
    breaks = seq(0, max(pts2), length.out = 12),
    predictor = x ~ exp(spde1D + Intercept)
  )


  # Plot them!
  attributes(bc)$ggp
}
}