R/data.shrimp.R

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

#' @name shrimp
#' @title Blue and red shrimp in the Western Mediterranean Sea
#' @docType data
#' @description Blue and red shrimp in the Western Mediterranean Sea.
#'
#' @usage data(shrimp)
#'
#' @format A list of objects:
#'  \describe{
#'    \item{`hauls`:}{ An `sf` object containing haul locations}
#'    \item{`mesh`:}{ An `fm_mesh_2d` object containing a Delaunay triangulation
#'      mesh (a type of discretization of continuous space) covering the haul
#'      locations.}
#'    \describe{
#'       \item{`catch`}{Catch in Kg.}
#'       \item{`landing`}{Landing in Kg.}
#'       \item{`depth`}{Mean depth (in metres) of the fishery haul.}
#'     }
#'  }
#' @source
#' Pennino, Maria Grazia. Personal communication.
#'
#' @references
#' Pennino, M. G., Paradinas, I., Munoz, F., Illian, J.,Quilez-Lopez, A.,
#' Bellido, J.M., Conesa, D. Accounting for preferential sampling in species
#' distribution models. Ecology and Evolution, 9(1), p653-663, 2019
#' <https://doi.org/10.1002/ece3.4789>
#'
#' @examples
#' \donttest{
#' if (require(ggplot2, quietly = TRUE)) {
#'   data(shrimp, package = "inlabru", envir = environment())
#'   ggplot() +
#'     geom_fm(data = shrimp$mesh) +
#'     gg(shrimp$hauls, aes(col = catch)) +
#'     coord_sf(datum = fm_crs(shrimp$hauls))
#' }
#' }
"shrimp"