Contour Detection Probability
Display contours of the net probability of detection p.(X), or the
area within a specified distance of detectors. buffer.contour
adds a conventional ‘boundary strip’ to a detector (trap) array, where
buffer equals the strip width.
pdot.contour(traps, border = NULL, nx = 64, detectfn = 0,
detectpar = list(g0 = 0.2, sigma = 25, z = 1), noccasions = NULL,
binomN = NULL, levels = seq(0.1, 0.9, 0.1), poly =
NULL, poly.habitat = TRUE, plt = TRUE, add = FALSE, fill = NULL, ...)
buffer.contour(traps, buffer, nx = 64, convex = FALSE, ntheta = 100,
plt = TRUE, add = FALSE, poly = NULL, poly.habitat = TRUE,
fill = NULL, ...)traps |
|
border |
width of blank margin around the outermost detectors |
nx |
dimension of interpolation grid in x-direction |
detectfn |
integer code or character string for shape of detection function 0 = halfnormal etc. – see detectfn |
detectpar |
list of values for named parameters of detection function |
noccasions |
number of sampling occasions |
binomN |
integer code for discrete distribution (see
|
levels |
vector of levels for p.(X) |
poly |
matrix of two columns, the x and y coordinates of a bounding polygon (optional) |
poly.habitat |
logical as in |
plt |
logical to plot contours |
add |
logical to add contour(s) to an existing plot |
fill |
vector of colours to fill contours (optional) |
... |
other arguments to pass to |
buffer |
vector of buffer widths |
convex |
logical, if TRUE the plotted contour(s) will be convex |
ntheta |
integer value for smoothness of convex contours |
pdot.contour constructs a rectangular mask and applies pdot to
compute the p.(X) at each mask point.
If convex = FALSE, buffer.contour constructs a mask and
contours the points on the basis of distance to the nearest detector at the
levels given in buffer.
If convex = TRUE, buffer.contour constructs a set of
potential vertices by adding points on a circle of radius =
buffer to each detector location; the desired contour is the
convex hull of these points (this algorithm derives from Efford, 2012).
If traps has a usage attribute then noccasions is
set accordingly; otherwise it must be provided.
If traps is for multiple sessions then detectpar should be a list
of the same length, one component per session, and noccasions may be a
numeric vector of the same length.
Increase nx for smoother lines, at the expense of speed.
Coordinates of the plotted contours are returned as a list with one
component per polygon. The list is returned invisibly if plt =
TRUE.
For multi-session input (traps) the value is a list of such
lists, one per session.
The precision (smoothness) of the fitted line in buffer.contour
is controlled by ntheta rather than nx when convex
= TRUE.
To suppress contour labels, include the argument drawlabels =
FALSE (this will be passed via ... to contour). Other useful
arguments of contour are col (colour of contour lines)
and lwd (line width).
You may wish to consider function gBuffer in package rgeos as an
alternative to buffer.contour..
buffer.contour failed with multi-session traps before
secr 2.8.0.
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand https://www.otago.ac.nz/density/.
possumtraps <- traps(possumCH)
plot(possumtraps, border = 270)
pdot.contour(possumtraps, detectfn = 0, nx = 128, detectpar =
detectpar(possum.model.0), levels = c(0.1, 0.01, 0.001),
noccasions = 5, add = TRUE)
## convex and concave buffers
plot(possumtraps, border = 270)
buffer.contour(possumtraps, buffer = 100, add = TRUE, col = "blue")
buffer.contour(possumtraps, buffer = 100, convex = TRUE, add = TRUE)
## areas
buff.concave <- buffer.contour(possumtraps, buffer = 100,
plt = FALSE)
buff.convex <- buffer.contour(possumtraps, buffer = 100,
plt = FALSE, convex = TRUE)
sum (sapply(buff.concave, polyarea)) ## sum over parts
sapply(buff.convex, polyarea)
## effect of nx on area
buff.concave2 <- buffer.contour(possumtraps, buffer = 100,
nx = 128, plt = FALSE)
sum (sapply(buff.concave2, polyarea))
## Not run:
## clipping to polygon
olddir <- setwd(system.file("extdata", package = "secr"))
possumtraps <- traps(possumCH)
possumarea <- read.table("possumarea.txt", header = TRUE)
par(xpd = TRUE, mar = c(1,6,6,6))
plot(possumtraps, border = 400, gridlines = FALSE)
pdot.contour(possumtraps, detectfn = 0, nx = 256, detectpar =
detectpar(possum.model.0), levels = c(0.1, 0.01, 0.001),
noccasions = 5, add = TRUE, poly = possumarea, col = "blue")
lines(possumarea)
setwd(olddir)
par(xpd = FALSE, mar = c(5,4,4,2) + 0.1) ## reset to default
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.