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),
...
)Arguments
- result
- 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 intervals per bin. Increase this if the bins are wide and the LGCP is not smooth.
- probs
numeric vector of probabilities with values in
[0,1]- ...
arguments passed on to
predict.bru()
Examples
if (FALSE) { # \dontrun{
if (require(ggplot2) && require(fmesher) && bru_safe_inla()) {
# 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")
matern <- INLA::inla.spde2.pcmatern(mesh1D,
prior.range = c(1, 0.01),
prior.sigma = c(1, 0.01),
constr = TRUE
)
mdl <- x ~ spde1D(x, model = matern) + Intercept(1)
fit.spde <- lgcp(mdl, pts2, domain = list(x = mesh1D))
# 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
}
} # }