Plot effective sampling area (Borchers and Efford 2008) as a function of increasing buffer width.
esa.plot (object, max.buffer = NULL, spacing = NULL, max.mask = NULL,
detectfn, detectpar, noccasions, binomN = NULL, thin = 0.1,
poly = NULL, poly.habitat = TRUE, session = 1, plt = TRUE,
type = c('density', 'esa', 'meanpdot', 'CVpdot'), n = 1, add = FALSE,
overlay = TRUE, conditional = FALSE, ...)A dataframe with columns
buffer width
computed effective sampling area
n/esa
\(p.(\mathbf{X})\)
cumulative minimum (\(p.(\mathbf{X})\))
expected pdot across mask (see CVpdot)
CV of pdot across mask (see CVpdot)
If plt = TRUE the dataframe is returned invisibly.
traps object or secr object output from secr.fit
maximum width of buffer in metres
distance between mask points
mask object
integer code or character string for shape of detection function 0 = halfnormal etc. -- see detectfn
list of values for named parameters of detection function
number of sampling occasions
integer code for discrete distribution (see
secr.fit)
proportion of mask points to retain in plot and output
matrix of two columns interpreted as the x and y coordinates of a bounding polygon (optional)
logical as in make.mask
vector of session indices (used if object spans multiple sessions)
logical to plot results
character, what to plot
integer number of distinct individuals detected
logical to add line to an existing plot
logical; if TRUE then automatically add = TRUE
for plots after the first
logical; if TRUE the reported mean and CV are conditional on detection
(see CVpdot)
graphical arguments passed to plot() and lines()
Effective sampling area (esa) is defined as the integral of net
capture probability (\(p.(\mathbf{X})\)) over a
region. esa.plot shows the effect of increasing region size on
the value of esa for fixed values of the detection parameters. The
max.buffer or max.mask arguments establish the maximum
extent of the region; points (cells) within this mask are sorted by
their distance \(d_k\) from the nearest detector. esa(buffer) is
defined as the cumulative sum of \(cp.(\mathbf{X})\) for
\(d_k(\mathbf{X}) <= \mbox{buffer}\), where \(c\)
is the area associated with each cell.
The default (type = 'density') is to plot the reciprocal of esa
multiplied by n; this is on a more familiar scale (the density
scale) and hence is easier to interpret.
Because esa.plot uses the criterion `distance to nearest
detector', max.mask should be constructed to include all
habitable cells within the desired maximum buffer and no others. This
is achieved with type = "trapbuffer" in make.mask. It is
a good idea to set the spacing argument of make.mask
rather than relying on the default based on nx. Spacing may be
small (e.g. sigma/10) and the buffer of max.mask may be quite
large (e.g. 10 sigma), as computation is fast.
Thinning serves to reduce redundancy in the plotted points, and (if
the result is saved and printed) to generate more legible numerical
output. Use thin=1 to include all points.
esa.plot calls the internal function esa.plot.secr when
object is a fitted model. In this case detectfn,
detectpar and noccasions are inferred from
object.
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture--recapture studies. Biometrics 64, 377--385.
mask, pdot, CVpdot,
make.mask, mask.check,
Detection functions
if (FALSE) {
## with previously fitted model
esa.plot(secrdemo.0)
## from scratch
trps <- make.grid()
msk <- make.mask(trps, buffer = 200, spacing = 5, type = "trapbuffer")
detectpar <- list(g0 = 0.2, sigma = 25)
esa.plot(trps,,, msk, 0, detectpar, nocc = 10, col = "blue")
esa.plot(trps,,, msk, 0, detectpar, nocc = 5, col = "green",
add = TRUE)
esa.plot(trps,,, msk, 0, detectpar, nocc = 5, thin = 0.002, plt = FALSE)
}
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