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amt (version 0.2.2.0)

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

[SpatRaster]
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

Run this code

# \donttest{
data(deer)
mini_deer <- deer[1:100, ]
trast <- make_trast(mini_deer)
md <- od(mini_deer, trast = trast)
terra::plot(md)

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

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