% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bru.gof.R
\name{bincount}
\alias{bincount}
\title{1D LGCP bin count simulation and comparison with data}
\usage{
bincount(result, predictor, observations, breaks, nint = 20,
  probs = c(0.025, 0.5, 0.975), ...)
}
\arguments{
\item{result}{A result object from a \link{bru} or \link{lgcp} call}

\item{predictor}{A formula describing the prediction of a 1D LGCP via \link{predict}.}

\item{observations}{A vector of observed values}

\item{breaks}{A vector of bin boundaries}

\item{nint}{Number of integration points per bin. Increase this if the bins are wide and}

\item{probs}{numeric vector of probabilities with values in [0,1]}

\item{...}{arguments passed on to \link{predict}}
}
\value{
An \link[INLA]{inla} object
}
\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.
}
\examples{

\dontrun{

# 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

}
}
