pdot {secr} | R Documentation

## 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)

    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)
`...` | 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.

`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’.

### Notes

`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.

### See Also

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

### 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)

      msk <- make.mask(temptrap, buffer = 100)
      CVpdot(msk, temptrap, detectpar = list(g0 = 0.2, sigma = 25),
        noccasions = 5)

[Package secr version 3.2.1 Index]