Description Usage Arguments Details Value Note See Also Examples
Compute spatially explicit net probability of detection for individual(s) at given coordinates.
1 2 3 4 5 
X 
vector or 2column matrix of coordinates 
traps 

detectfn 
integer code for detection function q.v. 
detectpar 
a named list giving a value for each parameter of detection function 
noccasions 
number of sampling intervals (occasions) 
binomN 
integer code for discrete distribution (see

userdist 
userdefined distance function or matrix (see userdist) 
ncores 
integer number of threads 
... 
arguments passed to 
conditional 
logical; if TRUE then computed mean and CV are conditional on detection 
If traps
has a usage attribute then noccasions
is
set accordingly; otherwise it must be provided.
The probability computed is p.(X) = 1  (1  prod(p_s(X,k))^S where
the product is over the detectors in traps
, excluding any not
used on a particular occasion. The peroccasion detection function
p_s is halfnormal (0) by default, and is assumed not to vary
over the S occasions.
For detection functions (10) and (11) the signal threshold ‘cutval’ should be
included in detectpar
, e.g., detectpar = list(beta0 = 103, beta1
= 0.11, sdS = 2, cutval = 52.5)
.
The calculation is not valid for singlecatch traps because p.(X) is reduced by competition between animals.
userdist
cannot be set if ‘traps’ is any of polygon, polygonX,
transect or transectX. if userdist
is a function requiring
covariates or values of parameters ‘D’ or ‘noneuc’ then X
must
have a covariates attribute with the required columns.
Setting ncores = NULL
uses the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS (see setNumThreads
).
CVpdot
returns the expected mean and CV of pdot across the points listed in X
, assuming uniform population density. X
is usually a habitat mask. See Notes for details.
For pdot
, a vector of probabilities, one for each row in X.
For CVpdot
, a named vector with elements ‘meanpdot’ and ‘CVpdot’.
CVpdot
computes the mean μ and variance V of the locationspecific overall detection probability p.(X) as follows.
μ = \int p.(X) f(X) dX,
V = \int p.(X)^2 f(X) dX  μ^2.
For uniform density and conditional = FALSE
, f(X) is merely a scaling factor independent of X.
If conditional = TRUE
then f(X) = p.(X) / \int p.(X) dX.
The coefficient of variation is CV = sqrt(V)/μ.
secr
,
make.mask
,
Detection functions
,
pdot.contour
,
CV
1 2 3 4 5 6 7 8 9 10 11 12 13  ## Not run:
temptrap < make.grid()
## persession detection probability for an individual centred
## at a corner trap. By default, noccasions = 5.
pdot (c(0,0), temptrap, detectpar = list(g0 = 0.2, sigma = 25),
noccasions = 5)
msk < make.mask(temptrap, buffer = 100)
CVpdot(msk, temptrap, detectpar = list(g0 = 0.2, sigma = 25),
noccasions = 5)
## End(Not run)

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