secr (version 3.0.1)

pdot: Net Detection Probability

Description

Compute spatially explicit net probability of detection for individual(s) at given coordinates.

Usage

pdot(X, traps, detectfn = 0, detectpar = list(g0 = 0.2,
    sigma = 25, z = 1), noccasions = NULL, binomN = NULL,
    userdist = NULL)

Arguments

X

vector or 2-column matrix of coordinates

traps

traps object

detectfn

integer code for detection function q.v.

detectpar

a list giving a value for each named parameter of detection function

noccasions

number of sampling intervals (occasions)

binomN

integer code for discrete distribution (see secr.fit)

userdist

user-defined distance function or matrix (see userdist)

Value

A vector of probabilities, one for each row in X.

Details

If traps has a usage attribute then noccasions is set accordingly; otherwise it must be provided.

The probability computed is \(p.(\mathbf{X}) = 1 - \prod\limits _{k} \{1 - p_s(\mathbf{X},k)\}^{S}\) 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 \(p.(\mathbf{X})\) 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.

See Also

secr, make.mask, Detection functions, pdot.contour

Examples

  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)