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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 histrogram 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),
  ...
)

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()`

Value

An data.frame with a ggplot attribute ggp

Examples

## Not run: 

# 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 = 50)
mesh1D <- inla.mesh.1d(x, boundary = "free")
mdl <- x ~ spde1D(map = x, model = inla.spde2.matern(mesh1D)) + Intercept # SOLUTION
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 = 12),
  predictor = x ~ exp(spde1D + Intercept)
)


# Plot them!
attributes(bc)$ggp

## End(Not run)

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