Estimate home ranges for tracked individuals or population
Source:R/ez_home_range.R
ez_home_range.RdComputes home range polygons using either Minimum Convex Polygon (MCP) or Kernel Density Estimation (KDE).
Usage
ez_home_range(
data,
method = "mcp",
level = 95,
crs = NULL,
population = FALSE,
start_date = NULL,
end_date = NULL,
kde_extent = 1,
h = "href",
hlim = NULL
)Arguments
- data
A data frame or `sf` object with columns `id`, `timestamp`, `x`, and `y`.
- method
Method for home range estimation. One of "mcp" (default) or "kde".
- level
Percentage of points to include in the home range (e.g., 95 for 95%). Default is 95.
- crs
Optional CRS to project the data before calculation. If `NULL`, the function will automatically detect the UTM zone of the dataset centroid and reproject data accordingly (units in meters).
- population
Logical. If TRUE, returns a single home range polygon for all data combined.
- start_date
Optional. A `Date` object or string ("YYYY-MM-DD"). Filters out data before this date.
- end_date
Optional. A `Date` object or string ("YYYY-MM-DD"). Filters out data after this date.
- kde_extent
Numeric. When method = "kde", passed to `adehabitatHR::kernelUD()` to control the extent of the grid for KDE. Default is 1.
- h
Bandwidth method when method = "kde". One of "href" (default), "LSCV", or a numeric value.
- hlim
Optional vector of length 2 passed to `adehabitatHR::kernelUD()` when method = "kde" to constrain the bandwidth search (used with h = "LSCV").