amt (version 0.1.2)

od: Occurrence Distribution

Description

od is a wrapper around ctmm::occurrence. See help(ctmm::occurrence) for more details. rolling_od estimates occurrence distributions for a subset of a track.

Usage

rolling_od(x, ...)
# S3 method for track_xyt
rolling_od(
  x,
  trast,
  model = fit_ctmm(x, "bm"),
  res.space = 10,
  res.time = 10,
  n.points = 5,
  show.progress = TRUE,
  ...
)
od(x, ...)
# S3 method for track_xyt
od(x, trast, model = fit_ctmm(x, "bm"), res.space = 10, res.time = 10, ...)

Arguments

x

[track_xyt] A track created with make_track that includes time.

...

Further arguments, none implemented.

trast

[RasterLayer] A template raster for the extent and resolution of the result.

model

[An output of fit_ctmm] The autocorrelation model that should be fit to the data. bm corresponds to Brownian motion, ou to an Ornstein-Uhlenbeck process, ouf to an Ornstein-Uhlenbeck forage process.

res.space

[numeric(1)=10] Number of grid point along each axis, relative to the average diffusion (per median timestep) from a stationary point. See also help(ctmm::occurrence).

res.time

[numeric(1)=10] Number of temporal grid points per median timestep.

n.points

[numeric(1)=5] This argument is only relevant for rolling_od and specifies the window size for the od estimation.

show.progress

[logical(1)=TRUE] Indicates if a progress bar is used.

References

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology.

Examples

# NOT RUN {
# }
# NOT RUN {
data(deer)
mini_deer <- deer[1:100, ]
trast <- make_trast(mini_deer)
md <- od(mini_deer, trast = trast)
raster::plot(md)

# rolling ud
xx <- rolling_od(mini_deer, trast)
# }