simMsOcc {spOccupancy} | R Documentation |
Simulate Multi-Species Detection-Nondetection Data
Description
The function simMsOcc
simulates multi-species detection-nondetection data for simulation studies, power assessments, or function testing. Data can be optionally simulated with a spatial Gaussian Process in the occurrence portion of the model, as well as an option to allow for species correlations using a factor modeling approach. Non-spatial random intercepts can also be included in the detection or occurrence portions of the occupancy model.
Usage
simMsOcc(J.x, J.y, n.rep, n.rep.max, N, beta, alpha, psi.RE = list(),
p.RE = list(), sp = FALSE, svc.cols = 1, cov.model,
sigma.sq, phi, nu, factor.model = FALSE, n.factors,
range.probs, shared.spatial = FALSE, grid, ...)
Arguments
J.x |
a single numeric value indicating the number of sites to simulate detection-nondetection data along the horizontal axis. Total number of sites with simulated data is |
J.y |
a single numeric value indicating the number of sites to simulate detection-nondetection data along the vertical axis. Total number of sites with simulated data is |
n.rep |
a numeric vector of length |
n.rep.max |
a single numeric value indicating the maximum number of replicate surveys. This is an optional argument, with its default value set to |
N |
a single numeric value indicating the number of species to simulate detection-nondetection data. |
beta |
a numeric matrix with |
alpha |
a numeric matrix with |
psi.RE |
a list used to specify the non-spatial random intercepts included in the occurrence portion of the model. The list must have two tags: |
p.RE |
a list used to specify the non-spatial random intercepts included in the detection portion of the model. The list must have two tags: |
sp |
a logical value indicating whether to simulate a spatially-explicit occupancy model with a Gaussian process. By default set to |
svc.cols |
a vector indicating the variables whose effects will be
estimated as spatially-varying coefficients. |
cov.model |
a quoted keyword that specifies the covariance function used to model the spatial dependence structure among the latent occurrence values. Supported covariance model key words are: |
sigma.sq |
a numeric vector of length |
phi |
a numeric vector of length |
nu |
a numeric vector of length |
factor.model |
a logical value indicating whether to simulate data following a factor modeling approach that explicitly incoporates species correlations. If |
n.factors |
a single numeric value specifying the number of latent factors to use to simulate the data if |
range.probs |
a numeric vector of length |
shared.spatial |
a logical value indicating used to specify whether a common spatial process should be estimated for all species instead of the factor modeling approach. |
grid |
an atomic vector used to specify the grid across which to simulate the latent spatial processes. This argument is used to simulate the underlying spatial processes at a different resolution than the coordinates (e.g., if coordinates are distributed across a grid). |
... |
currently no additional arguments |
Value
A list comprised of:
X |
a |
X.p |
a three-dimensional numeric array with dimensions corresponding to sites, repeat visits, and number of detection regression coefficients. This is the design matrix used for the detection portion of the occupancy model. |
coords |
a |
w |
a |
psi |
a |
z |
a |
p |
a |
y |
a |
X.p.re |
a three-dimensional numeric array containing the levels of any detection random effect included in the model. Only relevant when detection random effects are specified in |
X.lambda.re |
a numeric matrix containing the levels of any occurrence random effect included in the model. Only relevant when occurrence random effects are specified in |
alpha.star |
a numeric matrix where each row contains the simulated detection random effects for each given level of the random effects included in the detection model. Only relevant when detection random effects are included in the model. |
beta.star |
a numeric matrix where each row contains the simulated occurrence random effects for each given level of the random effects included in the occurrence model. Only relevant when occurrence random effects are included in the model. |
Author(s)
Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
Examples
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- sample(2:4, size = J, replace = TRUE)
N <- 10
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2, -0.15)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 0.3)
# Detection
alpha.mean <- c(0.5, 0.2)
tau.sq.alpha <- c(0.2, 0.3)
p.det <- length(alpha.mean)
psi.RE <- list(levels = c(10),
sigma.sq.psi = c(1.5))
p.RE <- list(levels = c(15),
sigma.sq.p = 0.8)
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = N, ncol = p.occ)
alpha <- matrix(NA, nrow = N, ncol = p.det)
for (i in 1:p.occ) {
beta[, i] <- rnorm(N, beta.mean[i], sqrt(tau.sq.beta[i]))
}
for (i in 1:p.det) {
alpha[, i] <- rnorm(N, alpha.mean[i], sqrt(tau.sq.alpha[i]))
}
# Spatial parameters if desired
phi <- runif(N, 3/1, 3/.1)
sigma.sq <- runif(N, 0.3, 3)
sp <- TRUE
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta,
alpha = alpha, psi.RE = psi.RE, p.RE = p.RE, sp = TRUE,
cov.model = 'exponential', phi = phi, sigma.sq = sigma.sq)