Net Detection Probability
Compute spatially explicit net probability of detection for individual(s) at given coordinates.
pdot(X, traps, detectfn = 0, detectpar = list(g0 = 0.2,
sigma = 25, z = 1), noccasions = NULL, binomN = NULL,
userdist = NULL, ncores = NULL)
CVpdot(..., conditional = FALSE)X |
vector or 2-column 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 |
user-defined 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 per-occasion 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 single-catch 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 location-specific 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.
temptrap <- make.grid()
## per-session 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.