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
- 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)) {
# 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(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 = 12),
predictor = x ~ exp(spde1D + Intercept)
)
# Plot them!
attributes(bc)$ggp
}
}
```