dbernppLocalACmovement_exp: Local evaluation of a Bernoulli point process for activity...

In nimbleSCR: Spatial Capture-Recapture (SCR) Methods Using 'nimble'

Local evaluation of a Bernoulli point process for activity center movement (exponential kernel)

Description

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

Usage

dbernppLocalACmovement_exp(
  x,
  lowerCoords,
  upperCoords,
  s,
  lambda = -999,
  rate,
  baseIntensities,
  habitatGrid,
  habitatGridLocal,
  resizeFactor = 1,
  localHabWindowIndices,
  numLocalHabWindows,
  numGridRows,
  numGridCols,
  numWindows,
  log = 0
)

rbernppLocalACmovement_exp(
  n,
  lowerCoords,
  upperCoords,
  s,
  lambda = -999,
  rate,
  baseIntensities,
  habitatGrid,
  habitatGridLocal,
  resizeFactor = 1,
  localHabWindowIndices,
  numLocalHabWindows,
  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. One row for each window. Each window should be of size 1x1 (after rescaling if necessary).
s	Vector of x- and y-coordinates of the isotropic multivariate exponential distribution mean (AC location at time t).
lambda	Rate parameter of the isotropic bivariate exponential distribution. Soon deprecated, use argument "rate" instead.
rate	Rate parameter of the isotropic bivariate exponential 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.
habitatGridLocal	Matrix of rescaled habitat grid cells indices, as returned by the getLocalObjects function (object named habitatGrid).
resizeFactor	Aggregation factor used in the getLocalObjects function to reduce the number of habitat grid cells.
localHabWindowIndices	Matrix of indices of local habitat windows around each local habitat grid cell (habitatGridLocal) from localIndices returned by getLocalObjects function.
numLocalHabWindows	Vector of numbers of local habitat windows around all habitat grid cells, from numLocalIndices returned by the getLocalObjects function. The ith number gives the number of local (original) habitat windows for the ith local habitat grid cell habitatGridLocal.
numGridRows, numGridCols	Numbers of rows and columns of the habitatGrid.
numWindows	Number of habitat windows. This value (positive integer) is 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 dbernppLocalACmovement_exp 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 exponential normal centered on the previous activity center (s) with rate (lambda). The local evaluation approach is implemented.

Value

The (log) probability density of the observation vector x.

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

C. Milleret, P. Dupont, C. Bonenfant, H. Broseth, O. Flagstad, C. Sutherland and R. Bischof. 2019. A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture. Ecology and Evolution 9:352-363

Examples

# Creat habitat grid
habitatGrid <- matrix(c(1:(4^2)), nrow = 4, ncol=4, byrow = TRUE)
coordsHabitatGridCenter <- matrix(c(0.5, 3.5,
                                    1.5, 3.5,
                                    2.5, 3.5,
                                    3.5, 3.5,
                                    0.5, 2.5,
                                    1.5, 2.5,
                                    2.5, 2.5,
                                    3.5, 2.5,
                                    0.5, 1.5,
                                    1.5, 1.5,
                                    2.5, 1.5,
                                    3.5, 1.5,
                                    0.5, 0.5,
                                    1.5, 0.5,
                                    2.5, 0.5,
                                    3.5, 0.5), ncol = 2,byrow = TRUE)
colnames(coordsHabitatGridCenter) <- c("x","y")
# Create habitat windows
lowerCoords <- coordsHabitatGridCenter-0.5
upperCoords <- coordsHabitatGridCenter+0.5
colnames(lowerCoords) <- colnames(upperCoords) <- c("x","y")
# Plot check
plot(lowerCoords[,"y"]~lowerCoords[,"x"],pch=16, xlim=c(0,4), ylim=c(0,4),col="red") 
points(upperCoords[,"y"]~upperCoords[,"x"],col="red",pch=16) 
points(coordsHabitatGridCenter[,"y"]~coordsHabitatGridCenter[,"x"],pch=16) 

# Rescale coordinates 
ScaledLowerCoords <- scaleCoordsToHabitatGrid(coordsData =  lowerCoords,
                                              coordsHabitatGridCenter = coordsHabitatGridCenter)
ScaledUpperCoords <- scaleCoordsToHabitatGrid(coordsData =  upperCoords,
                                              coordsHabitatGridCenter = coordsHabitatGridCenter)
ScaledUpperCoords$coordsDataScaled[,2] <- ScaledUpperCoords$coordsDataScaled[,2] + 1
ScaledLowerCoords$coordsDataScaled[,2] <- ScaledLowerCoords$coordsDataScaled[,2] - 1
habitatMask <- matrix(1, nrow = 4, ncol=4, byrow = TRUE)
# Create local objects 
HabWindowsLocal <- getLocalObjects(habitatMask = habitatMask,
                                   coords = coordsHabitatGridCenter,
                                   dmax=4,
                                   resizeFactor = 1,
                                   plot.check = TRUE
)

s <- c(1, 1) # Currrent activity center location
rate <- 0.1
numWindows <- nrow(coordsHabitatGridCenter)
baseIntensities <- rep(1,numWindows)
numRows <- nrow(habitatGrid)
numCols <- ncol(habitatGrid)

# The log probability density of moving from (1,1) to (1.2, 0.8) 
dbernppLocalACmovement_exp(x = c(1.2, 0.8),
 lowerCoords =lowerCoords,
 upperCoords = upperCoords,
 s =s,
 rate = rate,
 baseIntensities = baseIntensities,
 habitatGrid = habitatGrid, 
 habitatGridLocal = HabWindowsLocal$habitatGrid,
 resizeFactor = HabWindowsLocal$resizeFactor,
 localHabWindowIndices = HabWindowsLocal$localIndices,
 numLocalHabWindows = HabWindowsLocal$numLocalIndices,
 numGridRows = numRows,
 numGridCols = numCols,
 numWindows = numWindows,
 log = TRUE)

nimbleSCR documentation built on Dec. 1, 2022, 1:17 a.m.