R/data.toygroups.R

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

#' @name toygroups
#' @title Simulated 1D animal group locations and group sizes
#' @docType data
#' @description
#'
#' This data set serves to teach the concept of modelling species that gather in
#' groups and where the grouping behaviour depends on space.
#'
#' @usage data(toygroups)
#'
#' @format The data are a list that contains these elements:
#'  \describe{
#'    \item{`groups`:}{ A `data.frame` of group locations `x` and size `size`}
#'    \item{`df.size`:}{ IGNORE THIS }
#'    \item{`df.intensity`:}{ A `data.frame` with Poisson process
#'      intensity `d.lambda` at locations `x`}
#'    \item{`df.rate`:}{ A `data.frame` the locations `x` and associated `rate`
#'      which parameterized the exponential distribution from which the group
#'      sizes were drawn.}
#'  }
#'
#' @examples
#' \donttest{
#' if (require(ggplot2, quietly = TRUE)) {
#'   # Load the data
#'
#'   data("toygroups", package = "inlabru")
#'
#'   # The data set is a simulation of animal groups residing in a 1D space.
#'   # Their locations in x-space are sampled from a Cox process with
#'   # intensity
#'
#'   ggplot(toygroups$df.intensity) +
#'     geom_line(aes(x = x, y = g.lambda))
#'
#'   # Adding the simulated group locations to this plot we obtain
#'
#'   ggplot(toygroups$df.intensity) +
#'     geom_line(aes(x = x, y = g.lambda)) +
#'     geom_point(data = toygroups$groups, aes(x, y = 0), pch = "|")
#'
#'   # Each group has a size mark attached to it.
#'   # These group sizes are sampled from an exponential distribution
#'   # for which the rate parameter depends on the x-coordinate
#'
#'   ggplot(toygroups$groups) +
#'     geom_point(aes(x = x, y = size))
#'
#'   ggplot(toygroups$df.rate) +
#'     geom_line(aes(x, rate))
#' }
#' }
#'
"toygroups"