marginalVoidProbNumIntegration {nimbleSCR}

Marginal void probability

Description

Calculate the marginal void probability using the midpoint integration method.

Usage

marginalVoidProbNumIntegration(
  quadNodes,
  quadWeights,
  numNodes,
  lowerCoords,
  upperCoords,
  sd,
  baseIntensities,
  habIntensities,
  sumHabIntensity,
  numObsWindows,
  numHabWindows
)

Arguments

quadNodes

Three-dimensional array of nodes for midpoint integration. The dimension sizes are equal to the number of nodes per habitat window (1st), 2 (2nd), and the number of habitat windows (3rd).

quadWeights

Vector of weights for midpoint integration.

numNodes

Vector of numbers of nodes for all habitat windows.

lowerCoords, upperCoords

Matrix of lower and upper x- and y-coordinates of all detection windows. One row for each window.

sd

Standard deviation of the isotropic multivariate normal distribution.

baseIntensities

Vector of baseline detection intensities for all detection windows.

habIntensities

Vector of habitat intensities for all habitat windows.

sumHabIntensity

Total habitat selection intensity over all windows.

numObsWindows

Number of detection windows.

numHabWindows

Number of habitat windows.

Value

The marginal void probability.

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

lowerHabCoords <- matrix(c(0, 0, 0, 1), nrow = 2, byrow = TRUE)
upperHabCoords <- matrix(c(2, 1, 2, 2), nrow = 2, byrow = TRUE)
lowerObsCoords <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1), nrow = 4, byrow = TRUE)
upperObsCoords <- matrix(c(1, 1, 2, 1, 1, 2, 2, 2), nrow = 4, byrow = TRUE)
nodesRes <- getMidPointNodes(lowerHabCoords, upperHabCoords, 10)
quadNodes <- nodesRes$quadNodes
quadWeights <- nodesRes$quadWeights
numNodes <- rep(100, 2)
sd <- 0.1
baseDetIntensities <- c(1:4)
habIntensities <- c(1:2)
sumHabIntensity <- sum(habIntensities * c(2, 2))
numObsWindows <- 4
numHabWindows <- 2
marginalVoidProbNumIntegration(quadNodes, quadWeights, numNodes,
                               lowerObsCoords, upperObsCoords, sd, 
                               baseDetIntensities, habIntensities,
                               sumHabIntensity, numObsWindows, numHabWindows)