dbernppACmovement_normal | R Documentation |
Density and random generation functions of the Bernoulli point process for activity center movement between occasions based on a bivariate normal distribution.
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 )
x |
Vector of x- and y-coordinates of a single spatial point (typically AC location at time t+1) scaled to the habitat (see ( |
lowerCoords, upperCoords |
Matrices of lower and upper x- and y-coordinates of all habitat windows scaled to the habitat (see ( |
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 |
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 |
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. |
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).
dbernppACmovement_normal
gives the (log) probability density of the observation vector x
.
rbernppACmovement_normal
gives coordinates of a randomly generated spatial point.
Wei Zhang and Cyril Milleret
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
# 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)
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