simMsAbund {spAbundance}

Simulate Multivariate Data for Testing GLMMs

Description

The function simMsAbund simulates multivariate data without imperfect detection for simulation studies, power assessments, or function testing related to GLMMs. Data can be optionally simulated with a spatial Gaussian Process in the model, as well as an option to allow for species correlations using a factor modeling approach. Non-spatial random effects can also be included in the abundance portions of the model.

Usage

simMsAbund(J.x, J.y, n.rep, n.rep.max, n.sp, beta, kappa, tau.sq, mu.RE = list(), 
           offset = 1, sp = FALSE, cov.model, svc.cols = 1,
           sigma.sq, phi, nu, family = 'Poisson',
           factor.model = FALSE, n.factors, z, ...)

Arguments

J.x

a single numeric value indicating the number of sites to simulate count data along the horizontal axis. Total number of sites with simulated data is J.x \times J.y.

J.y

a single numeric value indicating the number of sites to simulate count data along the vertical axis. Total number of sites with simulated data is J.x \times J.y.

n.rep

a numeric vector of length J = J.x \times J.y indicating the number of replicate surveys at each of the J sites.

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 max(n.rep). This can be used to generate data sets with different types of missingness (e.g., simulate data across 20 days (replicate surveys) but sites are only sampled a maximum of ten times each).

n.sp

a single numeric value indicating the number of species to simulate count data.

beta

a numeric matrix with n.sp rows containing the intercept and regression coefficient parameters for the model. Each row corresponds to the regression coefficients for a given species.

kappa

a numeric vector of length n.sp containing the dispersion parameter for the model for each species. Only relevant when family = 'NB'.

tau.sq

a numeric vector of length n.sp containing the residual variance parameters for the model for each species. Only relevant for Gaussian or zero-inflated Gaussian models.

mu.RE

a list used to specify the non-spatial random intercepts included in the model. The list must have two tags: levels and sigma.sq.mu. levels is a vector of length equal to the number of distinct random intercepts to include in the model and contains the number of levels there are in each intercept. sigma.sq.mu is a vector of length equal to the number of distinct random intercepts to include in the model and contains the variances for each random effect. If not specified, no random effects are included in the model.

offset

either a single numeric value, a vector of length J, or a site by replicate matrix that contains the offset for each data point in the data set.

sp

a logical value indicating whether to simulate a spatially-explicit model with a Gaussian process. By default set to FALSE.

cov.model

a quoted keyword that specifies the covariance function used to model the spatial dependence structure among the abundance values. Supported covariance model key words are: "exponential", "matern", "spherical", and "gaussian".

svc.cols

a vector indicating the variables whose effects will be estimated as spatially-varying coefficients. svc.cols is an integer vector with values indicating the order of covariates specified in the model formula (with 1 being the intercept if specified).

sigma.sq

a numeric vector of length n.sp containing the spatial variance parameter for each species. Ignored when sp = FALSE or when factor.model = TRUE.

phi

a numeric vector of length n.sp containing the spatial decay parameter for each species. Ignored when sp = FALSE. If factor.model = TRUE, this should be of length n.factors.

nu

a numeric vector of length n.sp containing the spatial smoothness parameter for each species. Only used when sp = TRUE and cov.model = 'matern'. If factor.model = TRUE, this should be of length n.factors.

factor.model

a logical value indicating whether to simulate data following a factor modeling approach that explicitly incoporates species correlations. If sp = TRUE, the latent factors are simulated from independent spatial processes. If sp = FALSE, the latent factors are simulated from standard normal distributions.

n.factors

a single numeric value specifying the number of latent factors to use to simulate the data if factor.model = TRUE.

family

the distribution to use for the latent abundance process. Currently supports 'NB' (negative binomial) and 'Poisson'.

z

a matrix with n.sp rows and J columns containing the binary presence/absence portion of a zero-inflated Gaussian model for each species. Only relevant when family = 'zi-Gaussian'.

...

currently no additional arguments

Value

A list comprised of:

X

a three-dimensional numeric design array of covariates with dimensions corresponding to sites, replicates, and number of covariates (including an intercept) for the model.

coords

a J \times 2 numeric matrix of coordinates of each site. Required for spatial models.

w

a list of N \times J matrices of the spatially-varying coefficients for each species. Each element of the list corresponds to a different spatially-varying coefficient. Only used to simulate data when sp = TRUE. If factor.model = TRUE, the first dimension of each matrix is n.factors.

mu

a n.sp x J matrix of the mean abundances for each species at each site.

y

a n.sp x J x max(n.rep) array of the raw count data for each species at each site and replicate combination. Sites with fewer than max(n.rep) replicates will contain NA values.

X.re

a numeric matrix containing the levels of any abundance random effect included in the model. Only relevant when abundance random effects are specified in mu.RE.

beta.star

a numeric matrix where each row contains the simulated abundance random effects for each given level of the random effects included in the abundance model. Only relevant when abundance random effects are included in the model.

Author(s)

Jeffrey W. Doser [e-mail]

Examples

set.seed(408)
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep <- sample(3, size = J, replace = TRUE)
n.sp <- 6
# Community-level covariate effects
beta.mean <- c(-2, 0.5)
p.abund <- length(beta.mean)
tau.sq.beta <- c(0.2, 1.2)
# Random effects (two random intercepts)
mu.RE <- list(levels = c(10, 15),
              sigma.sq.mu = c(0.43, 0.5))
# Draw species-level effects from community means.
beta <- matrix(NA, nrow = n.sp, ncol = p.abund)
for (i in 1:p.abund) {
  beta[, i] <- rnorm(n.sp, beta.mean[i], sqrt(tau.sq.beta[i]))
}
sp <- TRUE
n.factors <- 2
factor.model <- TRUE
phi <- runif(n.factors, 3/1, 3 / .1)
kappa <- runif(n.sp, 0.1, 1)

dat <- simMsAbund(J.x = J.x, J.y = J.y, n.rep = n.rep, n.sp = n.sp, beta = beta,
                  mu.RE = mu.RE, sp = sp, kappa = kappa, family = 'NB',
                  factor.model = factor.model, phi = phi,
                  cov.model = 'spherical', n.factors = n.factors)