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and below). Secondly, it distinguishes sites in the vicinity of the
detector array that are `habitat' (i.e. have the potential to be
occupied) from `non-habitat'. Thirdly, it discretizes continuous habitat
as a list of points. Each point is notionally associated with a cell
(pixel) of uniform density. Discretization allows the SECR likelihood to
be evaluated by summing over grid cells. Fourthly, the x-y coordinates
of the mask and any habitat covariates may be used to build spatial
models of density. For example, a continuous or categorical habitat
covariate `cover' measured at each point on the mask might be used in a
formula for density such as D $\sim$cover.
In relation to the first purpose, the definition of `negligible' is
fluid. Any probability less than 0.001 seems OK in the sense of not
causing noticeable bias in density estimates, but this depends on the
shape of the detection function (fat-tailed functions such as `hazard
rate' are problematic). New tools for evaluating masks appeared in
mask.check, esa.plot), and
suggest.buffer automates selection of a buffer width.
Mask points are stored in a data frame with columns `x' and `y'. The
number of rows equals the number of points.
Possible mask attributes
covariates are generated automatically by
make.mask. Type `user' refers to masks input from a text file
with read.mask.
A virtual S4 class `mask' is defined to allow the definition of a method
for the generic function raster from the make.mask, read.mask,
mask.check, esa.plot,
suggest.buffer, secr.fit,
secr density models