dbernppAC {nimbleSCR}

Bernoulli point process for the distribution of activity centers

Description

Density and random generation functions of the Bernoulli point process for the distribution of activity centers.

Usage

dbernppAC(
  x,
  lowerCoords,
  upperCoords,
  logIntensities,
  logSumIntensity,
  habitatGrid,
  numGridRows,
  numGridCols,
  log = 0
)

rbernppAC(
  n,
  lowerCoords,
  upperCoords,
  logIntensities,
  logSumIntensity,
  habitatGrid,
  numGridRows,
  numGridCols
)

Arguments

x

Vector of x- and y-coordinates of a single spatial point (i.e. AC location) scaled to the habitat (see (scaleCoordsToHabitatGrid).

lowerCoords, upperCoords

Matrices of lower and upper x- and y-coordinates of all habitat windows scaled to the habitat (see (scaleCoordsToHabitatGrid). One row for each window. Each window should be of size 1x1.

logIntensities

Vector of log habitat intensities for all habitat windows.

logSumIntensity

Log of the sum of habitat intensities over all windows.

habitatGrid

Matrix of habitat window indices. Cell values should correspond to the order of habitat windows in lowerCoords, upperCoords, and logIntensities. When the habitat grid only consists of a single row or column of windows, an additional row or column of dummy indices has to be added because the nimble model code requires a matrix.

numGridRows, numGridCols

Numbers of rows and columns of the habitat grid.

log

Logical argument, specifying whether to return the log-probability of the distribution.

n

Integer specifying the number of realisations to generate. Only n = 1 is supported.

Details

The dbernppAC distribution is a NIMBLE custom distribution which can be used to model and simulate the activity center location (x) of a single individual in continuous space over a set of habitat windows defined by their upper and lower coordinates (lowerCoords,upperCoords). The distribution assumes that the activity center follows a Bernoulli point process with intensity = exp(logIntensities).

Value

dbernppAC gives the (log) probability density of the observation vector x. rbernppAC gives coordinates of a randomly generated spatial point.

Author(s)

Wei Zhang

References

W. Zhang, J. D. Chipperfield, J. B. Illian, P. Dupont, C. Milleret, P. de Valpine and R. Bischof. 2020. A hierarchical point process model for spatial capture-recapture data. bioRxiv. DOI 10.1101/2020.10.06.325035

Examples

# Use the distribution in R
lowerCoords <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1), nrow = 4, byrow = TRUE)
upperCoords <- matrix(c(1, 1, 2, 1, 1, 2, 2, 2), nrow = 4, byrow = TRUE)  
logIntensities <- log(c(1:4))
logSumIntensity <- log(sum(c(1:4)))  
habitatGrid <- matrix(c(1:4), nrow = 2, byrow = TRUE)
numGridRows <- nrow(habitatGrid)
numGridCols <- ncol(habitatGrid)
dbernppAC(c(0.5, 1.5), lowerCoords, upperCoords, logIntensities, logSumIntensity, 
          habitatGrid, numGridRows, numGridCols, log = TRUE)