dbernppACmovement_normal {nimbleSCR}

Bernoulli point process for activity center movement (normal kernel)

Description

Density and random generation functions of the Bernoulli point process for activity center movement between occasions based on a bivariate normal distribution.

Usage

dbernppACmovement_normal(
  x,
  lowerCoords,
  upperCoords,
  s,
  sd,
  baseIntensities,
  habitatGrid,
  numGridRows,
  numGridCols,
  numWindows,
  log = 0
)

rbernppACmovement_normal(
  n,
  lowerCoords,
  upperCoords,
  s,
  sd,
  baseIntensities,
  habitatGrid,
  numGridRows,
  numGridCols,
  numWindows
)

Arguments

x

Vector of x- and y-coordinates of a single spatial point (typically AC location at time t+1) 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.

s

Vector of x- and y-coordinates of the isotropic bivariate normal distribution mean (AC location at time t).

sd

Standard deviation of the isotropic bivariate normal distribution..

baseIntensities

Vector of baseline habitat intensities for all habitat windows.

habitatGrid

Matrix of habitat window indices. Cell values should correspond to the order of habitat windows in lowerCoords and upperCoords. 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.

numWindows

Number of habitat windows. This value (positive integer) can be used to truncate lowerCoords and upperCoords so that extra rows beyond numWindows are ignored.

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 dbernppACmovement_normal distribution is a NIMBLE custom distribution which can be used to model and simulate movement of activity centers between consecutive occasions in open population models. The distribution assumes that the new individual activity center location (x) follows an isotropic multivariate normal centered on the previous activity center (s) with standard deviation (sd).

Value

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

Author(s)

Wei Zhang and Cyril Milleret

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)  
s <- c(1, 1) # Currrent activity center location
sd <- 0.1
baseIntensities <- c(1:4)
habitatGrid <- matrix(c(1:4), nrow = 2, byrow = TRUE)
numRows <- nrow(habitatGrid)
numCols <- ncol(habitatGrid)
numWindows <- 4
# The log probability density of moving from (1,1) to (1.2, 0.8) 
dbernppACmovement_normal(c(1.2, 0.8), lowerCoords, upperCoords, s, sd, baseIntensities, 
                         habitatGrid, numRows, numCols, numWindows, log = TRUE)