Function for Fitting Multi-Season Single-Species Spatially-Varying Coefficient Binomial Models Using Polya-Gamma Latent Variables

The function svcTPGBinom fits multi-season single-species spatially-varying coefficient binomial models using Polya-Gamma latent variables. Models are fit using Nearest Neighbor Gaussian Processes.

Usage

svcTPGBinom(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, n.omp.threads = 1, 
            verbose = TRUE, ar1 = FALSE, n.report = 100, 
            n.burn = round(.10 * n.batch * batch.length), 
            n.thin = 1, n.chains = 1, k.fold, k.fold.threads = 1, 
            k.fold.seed = 100, k.fold.only = FALSE, ...)

Arguments

formula: a symbolic description of the model to be fit using R's model syntax. Only right-hand side of formula is specified. See example below. Random intercepts are allowed using lme4 syntax (Bates et al. 2015).
data: a list containing data necessary for model fitting. Valid tags are y, covs, weights, and coords. y is a two-dimensional array with the rows corresponding to the number of sites (\(J\)) and columns corresponding to the maximum number of primary time periods (i.e., years or seasons). covs is a list of variables included in the occurrence portion of the model. Each list element is a different occurrence covariate, which can be site level or site/primary time period level. Site-level covariates are specified as a vector of length \(J\) while site/primary time period level covariates are specified as a matrix with rows corresponding to sites and columns correspond to primary time periods. weights is a site by time period matrix containing the binomial weights (i.e., the total number of Bernoulli trials) at each site/time period combination. Note that missing values are allowed and should be specified as NA. coords is a \(J \times 2\) matrix of the observation coordinates. Note that spOccupancy assumes coordinates are specified in a projected coordinate system.
inits: a list with each tag corresponding to a parameter name. Valid tags are beta, sigma.sq, phi, w, nu, sigma.sq.psi, sigma.sq.t, and rho. nu is only specified if cov.model = "matern", and sigma.sq.psi is only specified if there are random effects in formula. sigma.sq.t and rho are only relevant when ar1 = TRUE. The value portion of each tag is the parameter's initial value. See priors description for definition of each parameter name. Additionally, the tag fix can be set to TRUE to fix the starting values across all chains. If fix is not specified (the default), starting values are varied randomly across chains.
priors: a list with each tag corresponding to a parameter name. Valid tags are beta.normal, phi.unif, sigma.sq.ig, sigma.sq.unif, nu.unif, sigma.sq.psi.ig, sigma.sq.t.ig, and rho.unif. Regression coefficients (beta) are assumed to follow a normal distribution. The hyperparameters of the normal distribution are passed as a list of length two with the first and second elements corresponding to the mean and variance of the normal distribution, which are each specified as vectors of length equal to the number of coefficients to be estimated or of length one if priors are the same for all coefficients. If not specified, prior means are set to 0 and prior variances set to 2.73. The spatial variance parameter, sigma.sq, for each spatially-varying coefficient is assumed to follow an inverse-Gamma distribution or a uniform distribution (default is inverse-Gamma). The spatial decay phi and smoothness nu parameters are assumed to follow Uniform distributions. The hyperparameters of the inverse-Gamma for sigma.sq are passed as a list with two elements corresponding to the shape and scale parametters, respetively, with each element comprised of a vector equal to the number of spatially-varying coefficients to be estimated or of length one if priors are the same for all coefficients. The hyperparameters of any uniform priors are also passed as a list of length two with the first and second elements corresponding to the lower and upper support, respectively, which can be passed as a vector equal to the total number of spatially-varying coefficients to be estimated or of length one if priors are the same for all coefficients. sigma.sq.psi are the random effect variances for any random effects, respectively, and are assumed to follow an inverse-Gamma distribution. The hyperparameters of the inverse-Gamma distribution are passed as a list of length two with the first and second elements corresponding to the shape and scale parameters, respectively, which are each specified as vectors of length equal to the number of random intercepts or of length one if priors are the same for all random effect variances. sigma.sq.t and rho are the AR(1) variance and correlation parameters for the AR(1) zero-mean temporal random effects, respectively. sigma.sq.t is assumed to follow an inverse-Gamma distribution, where the hyperparameters are specified as a vector with elements corresponding to the shape and scale parameters, respectively. rho is assumed to follow a uniform distribution, where the hyperparameters are specified in a vector of length two with elements corresponding to the lower and upper bounds of the uniform prior.
svc.cols: a vector indicating the variables whose effects will be estimated as spatially-varying coefficients. svc.cols can be an integer vector with values indicating the order of covariates specified in the model formula (with 1 being the intercept if specified), or it can be specified as a character vector with names corresponding to variable names in covs (for the intercept, use '(Intercept)').
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: "exponential", "matern", "spherical", and "gaussian".
tuning: a list with each tag corresponding to a parameter name. Valid tags are phi, sigma.sq, nu, and rho. The value portion of each tag defines the initial variance of the Adaptive sampler. See Roberts and Rosenthal (2009) for details.
NNGP: if TRUE, model is fit with an NNGP. If FALSE, a full Gaussian process is used. See Datta et al. (2016) and Finley et al. (2019) for more information. Currently, only NNGP = TRUE is supported for multi-season occupancy models.
n.neighbors: number of neighbors used in the NNGP. Only used if NNGP = TRUE. Datta et al. (2016) showed that 15 neighbors is usually sufficient, but that as few as 5 neighbors can be adequate for certain data sets, which can lead to even greater decreases in run time. We recommend starting with 15 neighbors (the default) and if additional gains in computation time are desired, subsequently compare the results with a smaller number of neighbors using WAIC or k-fold cross-validation.
search.type: a quoted keyword that specifies the type of nearest neighbor search algorithm. Supported method key words are: "cb" and "brute". The "cb" should generally be much faster. If locations do not have identical coordinate values on the axis used for the nearest neighbor ordering then "cb" and "brute" should produce identical neighbor sets. However, if there are identical coordinate values on the axis used for nearest neighbor ordering, then "cb" and "brute" might produce different, but equally valid, neighbor sets, e.g., if data are on a grid.
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.
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 n.omp.threads up to the number of hyperthreaded cores. Note, n.omp.threads > 1 might not work on some systems.
verbose: if TRUE, messages about data preparation, model specification, and progress of the sampler are printed to the screen. Otherwise, no messages are printed.
ar1: logical value indicating whether to include an AR(1) zero-mean temporal random effect in the model. If FALSE, the model is fit without an AR(1) temporal autocovariance structure. If TRUE, an AR(1) random effect is included in the model to account for temporal autocorrelation across the primary time periods.
n.report: the interval to report Metropolis sampler acceptance and MCMC progress.
n.burn: the number of samples out of the total n.batch * batch.length samples in each chain to discard as burn-in. By default, the first 10% of samples is discarded.
n.thin: the thinning interval for collection of MCMC samples. The thinning occurs after the n.burn samples are discarded. Default value is set to 1.
n.chains: the number of MCMC chains to run in sequence.
k.fold: specifies the number of k folds for cross-validation. If not specified as an argument, then cross-validation is not performed and k.fold.threads and k.fold.seed are ignored. In k-fold cross-validation, the data specified in data is randomly partitioned into k equal sized subsamples. Of the k subsamples, k - 1 subsamples are used to fit the model and the remaining k samples are used for prediction. The cross-validation process is repeated k times (the folds). As a scoring rule, we use the model deviance as described in Hooten and Hobbs (2015). For cross-validation in multi-season models, the data are split along the site dimension, such that each hold-out data set consists of a J / k.fold sites sampled over all primary time periods during which data are available at each given site. Cross-validation is performed after the full model is fit using all the data. Cross-validation results are reported in the k.fold.deviance object in the return list.
k.fold.threads: number of threads to use for cross-validation. If k.fold.threads > 1 parallel processing is accomplished using the foreach and doParallel packages. Ignored if k.fold is not specified.
k.fold.seed: seed used to split data set into k.fold parts for k-fold cross-validation. Ignored if k.fold is not specified.
k.fold.only: a logical value indicating whether to only perform cross-validation (TRUE) or perform cross-validation after fitting the full model (FALSE). Default value is FALSE.
...: currently no additional arguments

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 .

Finley, A. O., and Banerjee, S. (2020). Bayesian spatially varying coefficient models in the spBayes R package. Environmental Modelling and Software, 125, 104608.

Polson, N.G., J.G. Scott, and J. Windle. (2013) Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables. Journal of the American Statistical Association, 108:1339-1349.

Roberts, G.O. and Rosenthal J.S. (2009) Examples of adaptive MCMC. Journal of Computational and Graphical Statistics, 18(2):349-367.

Author

Jeffrey W. Doser doserjef@msu.edu,
Andrew O. Finley finleya@msu.edu

Value

An object of class svcTPGBinom that is a list comprised of:

beta.samples: a coda object of posterior samples for the regression coefficients.
y.rep.samples: a three-dimensional array of posterior samples for the fitted data values, with dimensions corresponding to posterior sample, site, and primary time period.
psi.samples: a three-dimensional array of posterior samples for the occurrence probability values, with dimensions corresponding to posterior sample, site, and primary time period.
theta.samples: a coda object of posterior samples for spatial covariance parameters and temporal covariance parameters if ar1 = TRUE.
w.samples: a three-dimensional array of posterior samples for the latent spatial random effects for all spatially-varying coefficients. Dimensions correspond to MCMC sample, coefficient, and sites.
sigma.sq.psi.samples: a coda object of posterior samples for variances of unstructured random intercepts included in the model. Only included if random intercepts are specified in formula.
beta.star.samples: a coda object of posterior samples for the unstructured random effects. Only included if random intercepts are specified in formula.
eta.samples: a coda object of posterior samples for the AR(1) random effects for each primary time period. Only included if ar1 = TRUE.
like.samples: a three-dimensional array of posterior samples for the likelihood values associated with each site and primary time period. Used for calculating WAIC.
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 proc.time().
k.fold.deviance: soring rule (deviance) from k-fold cross-validation. Only included if k.fold is specified in function call.

The return object will include additional objects used for subsequent prediction and/or model fit evaluation. Note that if k.fold.only = TRUE, the return list object will only contain run.time and k.fold.deviance

Examples

set.seed(1000)
# Sites
J.x <- 15
J.y <- 15 
J <- J.x * J.y
# Years sampled
n.time <- sample(10, J, replace = TRUE)
# Binomial weights
weights <- matrix(NA, J, max(n.time))
for (j in 1:J) {
  weights[j, 1:n.time[j]] <- sample(5, n.time[j], replace = TRUE)
}
# Occurrence --------------------------
beta <- c(-2, -0.5, -0.2, 0.75)
p.occ <- length(beta)
trend <- TRUE
sp.only <- 0
psi.RE <- list()
# Spatial parameters ------------------
sp <- TRUE
svc.cols <- c(1, 2, 3)
p.svc <- length(svc.cols)
cov.model <- "exponential"
sigma.sq <- runif(p.svc, 0.1, 1)
phi <- runif(p.svc, 3/1, 3/0.2)
# Temporal parameters -----------------
ar1 <- TRUE 
rho <- 0.8
sigma.sq.t <- 1

# Get all the data
dat <- simTBinom(J.x = J.x, J.y = J.y, n.time = n.time, weights = weights, beta = beta, 
                 psi.RE = psi.RE, sp.only = sp.only, trend = trend, 
                 sp = sp, svc.cols = svc.cols, 
                 cov.model = cov.model, sigma.sq = sigma.sq, phi = phi,
                 rho = rho, sigma.sq.t = sigma.sq.t, ar1 = TRUE, x.positive = FALSE)

# Prep the data for spOccupancy -------------------------------------------
y <- dat$y
X <- dat$X
X.re <- dat$X.re
coords <- dat$coords

# Package all data into a list
covs <- list(int = X[, , 1],
             trend = X[, , 2],
             cov.1 = X[, , 3], 
             cov.2 = X[, , 4])
# Data list bundle
data.list <- list(y = y,
                  covs = covs,
                  weights = weights, 
                  coords = coords)
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 2.72),
                   sigma.sq.ig = list(a = 2, b = 1),
                   phi.unif = list(a = 3/1, b = 3/.1), 
                   sigma.sq.t.ig = c(2, 0.5), 
                   rho.unif = c(-1, 1))

# Starting values
inits.list <- list(beta = beta, alpha = 0,
                   sigma.sq = 1, phi = 3 / 0.5, 
                   sigma.sq.t = 0.5, rho = 0)
# Tuning
tuning.list <- list(phi = 0.4, nu = 0.3, rho = 0.2)

# MCMC settings
n.batch <- 2
n.burn <- 0
n.thin <- 1

out <- svcTPGBinom(formula = ~ trend + cov.1 + cov.2, 
                   svc.cols = svc.cols,
                   data = data.list, 
                   n.batch = n.batch, 
                   batch.length = 25, 
                   inits = inits.list, 
                   priors = prior.list,
                   accept.rate = 0.43, 
                   cov.model = "exponential", 
                   ar1 = TRUE,
                   tuning = tuning.list, 
                   n.omp.threads = 1, 
                   verbose = TRUE, 
                   NNGP = TRUE, 
                   n.neighbors = 5,
                   n.report = 1, 
                   n.burn = n.burn, 
                   n.thin = n.thin, 
                   n.chains = 1) 
#> ----------------------------------------
#> 	Preparing the data
#> ----------------------------------------
#> ----------------------------------------
#> 	Building the neighbor list
#> ----------------------------------------
#> ----------------------------------------
#> Building the neighbors of neighbors list
#> ----------------------------------------
#> ----------------------------------------
#> 	Model description
#> ----------------------------------------
#> Spatial NNGP Multi-season Binomial Model with Polya-Gamma latent
#> variable fit with 225 sites and 10 years.
#> 
#> Samples per chain: 50 (2 batches of length 25)
#> Burn-in: 0 
#> Thinning Rate: 1 
#> Number of Chains: 1 
#> Total Posterior Samples: 50 
#> 
#> Number of spatially-varying coefficients: 3 
#> Using the exponential spatial correlation model.
#> 
#> Using 5 nearest neighbors.
#> 
#> Using an AR(1) temporal autocorrelation matrix.
#> 
#> Source compiled with OpenMP support and model fit using 1 thread(s).
#> 
#> Adaptive Metropolis with target acceptance rate: 43.0
#> ----------------------------------------
#> 	Chain 1
#> ----------------------------------------
#> Sampling ... 
#> Batch: 1 of 2, 50.00%
#> 	Coefficient	Parameter	Acceptance	Tuning
#> 	1		phi		68.0		0.40808
#> 	2		phi		72.0		0.40808
#> 	3		phi		44.0		0.40808
#> 	1		rho		96.0		0.20404
#> -------------------------------------------------
#> Batch: 2 of 2, 100.00%