R/data.Poisson1_1D.R

In inlabru-org/inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

#' @name Poisson1_1D
#' @title 1-Dimensional Homogeneous Poisson example.
#' @docType data
#' @description Point data and count data, together with intensity function and expected counts for
#' a homogeneous 1-dimensional Poisson process example.
#'
#' @aliases lambda1_1D E_nc1 pts1 countdata1
#'
#' @usage data(Poisson1_1D)
#'
#' @format The data contain the following `R` objects:
#'  \describe{
#'    \item{`lambda1_1D`:}{ A function defining the intensity function of a
#'    nonhomogeneous Poisson process. Note that this function is only defined on
#'    the interval (0,55).}
#'    \item{`E_nc1`}{ The expected counts of the gridded data.}
#'    \item{`pts1`}{ The locations of the observed points (a data frame with one column, named `x`).}
#'    \item{`countdata1`}{ A data frame with three columns, containing the count data:}
#'    \describe{
#'      \item{`x`}{ The grid cell midpoint.}
#'      \item{`count`}{ The number of detections in the cell.}
#'      \item{`exposure`}{ The width of the cell.}
#'    }
#'  }
#'
#' @examples
#' \donttest{
#' if (require("ggplot2", quietly = TRUE)) {
#'   data(Poisson1_1D)
#'   ggplot(countdata1) +
#'     geom_point(data = countdata1, aes(x = x, y = count), col = "blue") +
#'     ylim(0, max(countdata1$count)) +
#'     geom_point(data = pts1, aes(x = x), y = 0.2, shape = "|", cex = 4) +
#'     geom_point(
#'       data = countdata1, aes(x = x), y = 0, shape = "+",
#'       col = "blue", cex = 4
#'     ) +
#'     xlab(expression(bold(s))) +
#'     ylab("count")
#' }
#' }
NULL