R/deprecated/deprecated_calculate_random_distance.R

In mhesselbarth/rabmp: R agent-based model for plants

#' #' deprecated_calculate_random_distance
#' #'
#' #' @description Calculate random coords
#' #'
#' #' @param species Current species
#' #' @param n number of distances
#' #' @param max_dist maximum distance
#' #' @param number_samples random number used to generate distances
#' #'
#' #' @details
#' #' Calculates random distances following the probability function of a species-specific
#' #' seed kernel. Because a 'brute-force' approach is used, computational demanding
#' #'
#' #' @return vector
#' #'
#' #' @examples
#' #' deprecated_calculate_random_distance(species = "Beech", n = 10)
#' #'
#' #' @aliases deprecated_calculate_random_distance
#' #' @rdname deprecated_calculate_random_distance
#' #'
#' #' @references
#' #' Ribbens, E., Silander, J. A., & Pacala, S. W. (1994). Seedling recruitment in forests:
#' #' Calibrating models to predict patterns of tree seedling dispersion. Ecology, 75(6), 1794-1806.
#' #'
#' #' @export
#' deprecated_calculate_random_distance <- function(species, n = NULL, max_dist = 80, number_samples = 1000000) {
#'
#'   # random numbers between 0 and max dispersal distance
#'   proposed_coords <- tibble::tibble(proposed = runif(number_samples,
#'                                                      min = 0, max = max_dist))
#'
#'   # Create coordinates according to kernel
#'   proposed_coords <- dplyr::mutate(proposed_coords,
#'                                    kernel = deprecated_calculate_seed_kernel(species = species,distance = proposed,
#'                                                                              max_dist = max_dist), # probability of random number according to seed dipersal
#'                                    target = stats::runif(number_samples, min = 0, max = 1), # test value
#'                                    accept = dplyr::case_when(target <= kernel / max(kernel, na.rm = TRUE) ~ TRUE, # set TRUE for numbers that fit distribution
#'                                                              TRUE ~ FALSE))
#'   #
#'   coords_filterd <- dplyr::filter(proposed_coords, accept == TRUE) # only coordinates that fit the distribution
#'
#'   # Distance in both directions
#'   coords <- c(dplyr::pull(coords_filterd, proposed),
#'               dplyr::pull(coords_filterd, proposed) * -1)
#'
#'   if(is.null(n)){n <- length(result)} # no provided, returns all coordinates
#'
#'   result <- sample(coords, n) # only n coordinates
#'
#'   return(result)
#' }