# pdot: Net Detection Probability In secr: Spatially Explicit Capture-Recapture

## Description

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

## Usage

 1 2 3 4 5 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) 

## Arguments

 X vector or 2-column matrix of coordinates traps traps object 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 secr.fit) userdist user-defined distance function or matrix (see userdist) ncores integer number of threads ... arguments passed to pdot conditional logical; if TRUE then computed mean and CV are conditional on detection

## Details

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.

## Value

For pdot, a vector of probabilities, one for each row in X.

For CVpdot, a named vector with elements ‘meanpdot’ and ‘CVpdot’.

## Note

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.

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() ## 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) ## End(Not run)