R/hr_documentation.R

In amt: Animal Movement Tools

#' Home ranges
#'
#' Functions to calculate animal home ranges from a `track_xy*`. `hr_mcp`, `hr_kde`, and `hr_locoh` calculate the minimum convex
#' polygon, kernel density, and local convex hull home range respectively.
#'
#' @template track_xy_star
#' @param levels `[numeric]` \cr The isopleth levels used for calculating home
#'   ranges. Should be `0 < level < 1`.
#' @param n `[integer(1)]` \cr The number of neighbors used when calculating
#'   local convex hulls.
#' @param rand_buffer `[numeric(1)]` \cr Random buffer to avoid polygons with
#'   area 0 (if coordinates are numerically identical).
#' @param trast `[SpatRast]` \cr A template raster for kernel density home-ranges.
#' @param keep.data `[logic(1)]` \cr Should the original tracking data be included in the estimate?
#' @param model A continuous time movement model. This can be fitted either with `ctmm::ctmm.fit` or `fit_ctmm`.
#' @param wrap `[logical(1)]` \cr If `TRUE` the UD is wrapped (see `terra::wrap()`).
#' @template dots_none
#' @param h `[numeric(2)]` \cr The bandwidth for kernel density estimation.
#' @return A `hr`-estimate.
#' @references Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. _Ecology, 70(1)_, 164-168.
#' C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, “Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator”, Ecology, 96:5, 1182-1188 (2015).
#' @name hrest
#' @examples
#' \donttest{
#' data(deer)
#' mini_deer <- deer[1:100, ]
#'
#' # MCP ---------------------------------------------------------------------
#' mcp1 <- hr_mcp(mini_deer)
#' hr_area(mcp1)
#'
#' # calculated MCP at different levels
#' mcp1 <- hr_mcp(mini_deer, levels = seq(0.3, 1, 0.1))
#' hr_area(mcp1)
#'
#' # CRS are inherited
#' get_crs(mini_deer)
#' mcps <- hr_mcp(mini_deer, levels = c(0.5, 0.95, 1))
#' has_crs(mcps)
#'
#' # Kernel density estimaiton (KDE) -----------------------------------------
#' kde1 <- hr_kde(mini_deer)
#' hr_area(kde1)
#' get_crs(kde1)
#'
#' # akde
#' data(deer)
#' mini_deer <- deer[1:20, ]
#' ud1 <- hr_akde(mini_deer) # uses an iid ctmm
#' ud2 <- hr_akde(mini_deer, model = fit_ctmm(deer, "ou")) # uses an OU ctmm
#' }

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