simTMsOcc {spOccupancy} | R Documentation |
Simulate Multi-Species Multi-Season Detection-Nondetection Data
Description
The function simTMsOcc
simulates multi-species multi-season 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
simTMsOcc(J.x, J.y, n.time, n.rep, N, beta, alpha, sp.only = 0,
trend = TRUE, psi.RE = list(), p.RE = list(),
sp = FALSE, svc.cols = 1, cov.model,
sigma.sq, phi, nu, ar1 = FALSE, rho, sigma.sq.t,
factor.model = FALSE, n.factors, range.probs, 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.time |
a single numeric value indicating the number of primary time periods (denoted T) over which sampling occurs. |
n.rep |
a numeric matrix indicating the number of replicates at each site during each primary time period. The matrix must have |
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 |
sp.only |
a numeric vector specifying which occurrence covariates should only vary over space and not over time. The numbers in the vector correspond to the elements in the vector of regression coefficients ( |
trend |
a logical value. If |
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 |
ar1 |
a logical value indicating whether to simulate a temporal random effect with an AR(1) process. By default, set to |
rho |
a vector of |
sigma.sq.t |
a vector of |
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 |
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 four-dimensional numeric array with dimensions corresponding to sites, primary time periods, 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 four-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.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. |
eta |
a numeric matrix with each row corresponding to species and column corresponding to time period of the AR(1) temporal random effects. |
Author(s)
Jeffrey W. Doser doserjef@msu.edu,
Examples
# Simulate Data -----------------------------------------------------------
set.seed(500)
J.x <- 8
J.y <- 8
J <- J.x * J.y
# Years sampled
n.time <- sample(3:10, J, replace = TRUE)
# n.time <- rep(10, J)
n.time.max <- max(n.time)
# Replicates
n.rep <- matrix(NA, J, max(n.time))
for (j in 1:J) {
n.rep[j, 1:n.time[j]] <- sample(2:4, n.time[j], replace = TRUE)
# n.rep[j, 1:n.time[j]] <- rep(4, n.time[j])
}
N <- 7
# Community-level covariate effects
# Occurrence
beta.mean <- c(-3, -0.2, 0.5)
trend <- FALSE
sp.only <- 0
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.5, 1.4)
# Detection
alpha.mean <- c(0, 1.2, -1.5)
tau.sq.alpha <- c(1, 0.5, 2.3)
p.det <- length(alpha.mean)
# Random effects
psi.RE <- list()
p.RE <- list()
# 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]))
}
sp <- TRUE
svc.cols <- c(1, 2)
p.svc <- length(svc.cols)
n.factors <- 3
phi <- runif(p.svc * n.factors, 3 / .9, 3 / .3)
factor.model <- TRUE
cov.model <- 'exponential'
dat <- simTMsOcc(J.x = J.x, J.y = J.y, n.time = n.time, n.rep = n.rep, N = N,
beta = beta, alpha = alpha, sp.only = sp.only, trend = trend,
psi.RE = psi.RE, p.RE = p.RE, factor.model = factor.model,
svc.cols = svc.cols, n.factors = n.factors, phi = phi, sp = sp,
cov.model = cov.model)
str(dat)