dpoisppAC | R Documentation |
Density and random generation functions of the Poisson point process for the distribution of activity centers.
The dpoisppAC
distribution is a NIMBLE custom distribution which
can be used to model and simulate activity center locations (x) of multiple individual in
continuous space over a set of habitat windows defined by their upper and lower coordinates (lowerCoords,upperCoords).
The distribution assumes that activity centers follow a Poisson point process with intensity = exp(logIntensities).
All coordinates (s and trapCoords) should be scaled to the habitat (scaleCoordsToHabitatGrid
).
dpoisppAC( x, lowerCoords, upperCoords, logIntensities, sumIntensity, habitatGrid, numGridRows, numGridCols, numPoints, log = 0 ) rpoisppAC( n, lowerCoords, upperCoords, logIntensities, sumIntensity, habitatGrid, numGridRows, numGridCols, numPoints )
x |
Matrix of x- and y-coordinates of a set of spatial points (AC locations) scaled to the habitat ( |
lowerCoords, upperCoords |
Matrices of lower and upper x- and y-coordinates of all detection windows scaled to the habitat (see ( |
logIntensities |
Vector of log habitat intensities for all habitat windows. |
sumIntensity |
Sum of the habitat intensities over all windows. Provided as an argument for computational speed, instead of calculating it in the function. |
habitatGrid |
Matrix of habitat window indices. Cell values should correspond to the order of habitat windows in
|
numGridRows, numGridCols |
Numbers of rows and columns of the habitat grid. |
numPoints |
Number of points in the Poisson point process. This value (non-negative integer) is used to truncate |
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. |
dpoisppAC
gives the (log) probability density of the observation matrix x
.
rpoisppAC
gives coordinates of a set of randomly generated spatial points.
Wei Zhang
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
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 <- sum(exp(logIntensities)) habitatGrid <- matrix(c(1:4), nrow = 2, byrow = TRUE) numGridRows <- nrow(habitatGrid) numGridCols <- ncol(habitatGrid) #Simulate data x <- rpoisppAC(1, lowerCoords, upperCoords, logIntensities, logSumIntensity, habitatGrid, numGridRows, numGridCols, -1) numPoints <- nrow(x) dpoisppAC(x, lowerCoords, upperCoords, logIntensities, logSumIntensity, habitatGrid, numGridRows, numGridCols, numPoints, log = TRUE)
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