svcAbund {spAbundance} | R Documentation |
Function for Fitting Univariate Spatialy-Varying Coefficient GLMMs
Description
The function svcAbund
fits univariate spatially-varying coefficient GLMMs.
Usage
svcAbund(formula, data, inits, priors, tuning,
svc.cols = 1, cov.model = 'exponential', NNGP = TRUE,
n.neighbors = 15, search.type = 'cb', n.batch,
batch.length, accept.rate = 0.43, family = 'Gaussian',
n.omp.threads = 1, verbose = TRUE, n.report = 100,
n.burn = round(.10 * n.batch * batch.length), n.thin = 1,
n.chains = 1, ...)
Arguments
formula |
a symbolic description of the model to be fit for the model using R's model syntax. Only right-hand side of formula is specified. See example below. Random intercepts and slopes are allowed using lme4 syntax (Bates et al. 2015). |
data |
a list containing data necessary for model fitting.
Valid tags are |
inits |
a list with each tag corresponding to a parameter name.
Valid tags are |
priors |
a list with each tag corresponding to a parameter name.
Valid tags are |
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
observations. Supported covariance model key words are:
|
tuning |
a single numeric value representing the initial variance of the
adaptive sampler for |
NNGP |
if |
n.neighbors |
number of neighbors used in the NNGP. Only used if
|
search.type |
a quoted keyword that specifies the type of nearest
neighbor search algorithm. Supported method key words are: |
n.batch |
the number of MCMC batches in each chain to run for the adaptive MCMC sampler. See Roberts and Rosenthal (2009) for details. |
batch.length |
the length of each MCMC batch in each chain to run for the adaptive MCMC sampler. See Roberts and Rosenthal (2009) for details. |
accept.rate |
target acceptance rate for adaptive MCMC. Default is 0.43. See Roberts and Rosenthal (2009) for details. |
family |
the distribution to use for abundance. Currently, spatially-varying
coefficient models are available for |
n.omp.threads |
a positive integer indicating
the number of threads to use for SMP parallel processing. The package must
be compiled for OpenMP support. For most Intel-based machines, we
recommend setting |
verbose |
if |
n.report |
the interval to report Metropolis sampler acceptance and MCMC progress. |
n.burn |
the number of samples out of the total |
n.thin |
the thinning interval for collection of MCMC samples. The
thinning occurs after the |
n.chains |
the number of MCMC chains to run in sequence. |
... |
currently no additional arguments |
Value
An object of class svcAbund
that is a list comprised of:
beta.samples |
a |
tau.sq.samples |
a |
y.rep.samples |
a |
mu.samples |
a |
theta.samples |
a |
w.samples |
a three-dimensional array of posterior samples for the spatially-varying coefficients with dimensions corresponding to MCMC sample, SVC, and site. |
sigma.sq.mu.samples |
a |
beta.star.samples |
a |
like.samples |
a |
rhat |
a list of Gelman-Rubin diagnostic values for some of the model parameters. |
ESS |
a list of effective sample sizes for some of the model parameters. |
run.time |
execution time reported using |
The return object will include additional objects used for subsequent prediction and/or model fit evaluation.
Author(s)
Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu
References
Bates, Douglas, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
Datta, A., S. Banerjee, A.O. Finley, and A.E. Gelfand. (2016) Hierarchical Nearest-Neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, doi:10.1080/01621459.2015.1044091.
Finley, A.O., A. Datta, B.D. Cook, D.C. Morton, H.E. Andersen, and S. Banerjee. (2019) Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes. Journal of Computational and Graphical Statistics, doi:10.1080/10618600.2018.1537924.
Roberts, G.O. and Rosenthal J.S. (2009) Examples of adaptive MCMC. Journal of Computational and Graphical Statistics, 18(2):349-367.
Examples
set.seed(1000)
# Sites
J.x <- 10
J.y <- 10
J <- J.x * J.y
# Abundance ---------------------------
beta <- c(5, 0.5, -0.2, 0.75)
p <- length(beta)
mu.RE <- list()
mu.RE <- list(levels = c(35, 40),
sigma.sq.mu = c(0.7, 1.5),
beta.indx = list(1, 1))
# Spatial parameters ------------------
sp <- TRUE
svc.cols <- c(1, 2)
p.svc <- length(svc.cols)
cov.model <- "exponential"
sigma.sq <- runif(p.svc, 0.4, 4)
phi <- runif(p.svc, 3/1, 3/0.6)
tau.sq <- 2
z <- rbinom(J, 1, 0.5)
# Get all the data
dat <- simAbund(J.x = J.x, J.y = J.y, beta = beta, tau.sq = tau.sq,
mu.RE = mu.RE, sp = sp, svc.cols = svc.cols,
family = 'zi-Gaussian', cov.model = cov.model,
sigma.sq = sigma.sq, phi = phi, z = z)
# Get data in format for spAbundance --------------------------------------
y <- dat$y
X <- dat$X
X.re <- dat$X.re
coords <- dat$coords
# Package all data into a list
covs <- cbind(X, X.re)
colnames(covs) <- c('int', 'cov.1', 'cov.2', 'cov.3', 'factor.1', 'factor.2')
# Data list bundle
data.list <- list(y = y, covs = covs, coords = coords, z = z)
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 1000),
sigma.sq.ig = list(a = 2, b = 1), tau.sq = c(2, 1),
sigma.sq.mu.ig = list(a = 2, b = 1),
phi.unif = list(a = 3 / 1, b = 3 / 0.1))
# Starting values
inits.list <- list(beta = 0, alpha = 0,
sigma.sq = 1, phi = 3 / 0.5,
tau.sq = 2, sigma.sq.mu = 0.5)
# Tuning
tuning.list <- list(phi = 1)
n.batch <- 10
batch.length <- 25
n.burn <- 100
n.thin <- 1
out <- svcAbund(formula = ~ cov.1 + cov.2 + cov.3 +
(1 | factor.1) + (1 | factor.2),
svc.cols = c(1, 2),
data = data.list,
n.batch = n.batch,
batch.length = batch.length,
inits = inits.list,
priors = prior.list,
accept.rate = 0.43,
family = 'zi-Gaussian',
cov.model = "exponential",
tuning = tuning.list,
n.omp.threads = 1,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = 25,
n.burn = n.burn,
n.thin = n.thin,
n.chains = 3)