Estimates animal density (amongst other parameters) from two-camera aerial surveys. This conceptualises sighting locations as a Neyman-Scott point pattern.
fit.twocamera(
points,
cameras = NULL,
d,
w,
b,
l,
tau,
R,
edge.correction = "pbc",
start = NULL,
bounds = NULL,
trace = FALSE
)An R6 reference class object.
A vector (or single-column matrix) containing the distance along the transect that each detection was made.
An optional vector containing the camera ID (either
1 or 2) that made the corresponding detection in
points.
The length of the transect flown (in km).
The distance from the transect to which detection of individuals on the surface is certain. This is equivalent to the half-width of the detection zone.
The distance from the transect to the edge of the area of interest. Conceptually, the distance between the transect and the furthest distance a whale could be on the passing of the first camera and plausibly move into the detection zone by the passing of the second camera.
The lag between cameras (in seconds).
Mean dive-cycle duration (in seconds).
Truncation distance (see fit.ns).
The method used for the correction of edge
effects. Either "pbc" for periodic boundary conditions,
or "buffer" for a buffer-zone correction.
A named vector of starting values for the model parameters.
A list with named components. Each component should be a vector of length two, giving the upper and lower bounds for the named parameter.
Logical; if TRUE, parameter values are printed
to the screen for each iteration of the optimisation procedure.
This function is simply a wrapper for fit.ns, and
facilitates the fitting of the model proposed by Stevenson,
Borchers, and Fewster (2019). This function presents the
parameter D.2D (two-dimensional cetacean density in
cetaceans per square km) rather than D for enhanced
interpretability.
For further details on the cluster capture-recapture estimation approach, see Fewster, Stevenson and Borchers (2016).
Fewster, R. M., Stevenson, B. C., and Borchers, D. L. (2016) Trace-contrast methods for capture-recapture without capture histories. Statistical Science, 31: 245--258.
Stevenson, B. C., Borchers, D. L., and Fewster, R. M. (2019) Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics, 75: 326--336.
Use coef.palm to extract estimated parameters, and plot.palm to plot the estimated Palm intensity function. Use boot.palm to run a parametric bootstrap, allowing calculation of standard errors and confidence intervals.
See sim.twocamera to simulate sightings from a two-camera aerial survey.
## Fitting model.
fit <- fit.twocamera(points = example.twocamera$points, cameras = example.twocamera$cameras,
d = 500, w = 0.175, b = 0.5, l = 20, tau = 110, R = 1)
## Printing estimates.
coef(fit)
## Plotting the estimated Palm intensity.
plot(fit)
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