# Function for Fitting Multi-Season Single-Species Spatial Integrated Occupancy Models Using Polya-Gamma Latent Variables Function for fitting single-species multi-season spatial integrated occupancy models using Polya-Gamma latent variables. Data integration is done using a joint likelihood framework, assuming distinct detection models for each data source that are each conditional on a single latent occurrence process. Models are fit using Nearest Neighbor Gaussian Processes. ```r stIntPGOcc(occ.formula, det.formula, data, inits, priors, tuning, 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, ...) ``` ## Arguments - `occ.formula`: a symbolic description of the model to be fit for the occurrence portion of the model 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). - `det.formula`: a list of symbolic descriptions of the models to be fit for the detection portion of the model using R's model syntax for each data set. Each element in the list is a formula for the detection model of a given data set. 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`, `occ.covs`, `det.covs`, `sites`, `seasons`, and `coords`. `y` is a list of three-dimensional arrays with first dimensional equal to the number of sites surveyed in that data set, second dimension equal to the number of primary time periods (i.e., years or seasons), and third dimension equal to the maximum number of replicate surveys at a site within a given season. `occ.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 corresponding to primary time periods. `det.covs` is a list of variables included in the detection portion of the model for each data source. `det.covs` should have the same number of elements as `y`, where each element is itself a list. Each element of the list for a given data source is a different detection covariate, which can be site-level , site-season-level, or observation-level. Site-level covariates and site/primary time period level covariates are specified in the same manner as `occ.covs`. Observation-level covariates are specified as a three-dimensional array with first dimension corresponding to sites, second dimension corresponding to primary time period, and third dimension corresponding to replicate. `sites` is a list of site indices with number of elements equal to the number of data sources being modeled. Each element contains a vector of length equal to the number of sites that specific data source contains. Each value in the vector indicates the corresponding site in `occ.covs` covariates that corresponds with the specific row of the detection-nondetection data for the data source. This is used to properly link sites across data sets. Similarly, `seasons` is a list of season indices with number of elements equal to the number of data sources being modeled. Each element contains a vector of length equal to the number of seasons that a specific data source is available for. This is used to properly link seasons across data sets. Each value in the vector indicates the corresponding season in `occ.covs` covariates that correspond with the specific column of the detection-nondetection data for the given data source. This is used to properly link seasons across data sets, which can have a differing number of seasons surveyed. `coords` is a matrix of the observation site 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 `z`, `beta`, `alpha`, `sigma.sq.psi`, `sigma.sq.p`, `sigma.sq.t`, `rho`, `phi`, `w`, `nu`, `sigma.sq`. The value portion of each tag is the parameter's initial value. `sigma.sq.psi` and `sigma.sq.p` are only relevant when including random effects in the occurrence and detection portion of the occupancy model, respectively. `sigma.sq.t` and `rho` are only relevant when `ar1 = TRUE`. The tag `alpha` is a list comprised of the initial values for the detection parameters for each data source. Each element of the list should be a vector of initial values for all detection parameters in the given data source or a single value for each data source to assign all parameters for a given data source the same 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`, `alpha.normal`, `sigma.sq.psi.ig`, `sigma.sq.p.ig`, `sigma.sq.t.ig`, `rho.unif`, `phi.unif`, `nu.unif`, `sigma.sq.ig`, and `sigma.sq.unif`. Occupancy (`beta`) and detection (`alpha`) regression coefficients are assumed to follow a normal distribution. For `beta` 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. For the detection coefficients `alpha`, the mean and variance hyperparameters are themselves passed in as lists, with each element of the list corresponding to the specific hyperparameters for the detection parameters in a given data source. If not specified, prior means are set to 0 and prior variances set to 2.72. `sigma.sq.psi` and `sigma.sq.p` are the random effect variances for any unstructured occurrence or detection 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 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. `sigma.sq`, 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 are passed as a vector of length two, with the first and second elements corresponding to the **shape** and **scale**, respectively. The hyperparameters of the Uniform are also passed as a vector of length two with the first and second elements corresponding to the lower and upper support, respectively. - `tuning`: a list with each tag corresponding to a parameter name. Valid tags are `rho`, `phi`, and `nu`. The value portion of each tag defines the initial tuning variance of the Adaptive sampler. See Roberts and Rosenthal (2009) for details. - `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"`. - `NNGP`: if `TRUE`, model is fit with an NNGP. If `FALSE`, a full Gaussian process is used. Currently only NNGP models are supported. - `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. - `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 within chains. 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. Currently only relevant for spatial models. - `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 MCMC progress. Note this is specified in terms of batches, not MCMC samples. - `n.burn`: the number of samples out of the total `n.samples` to discard as burn-in for each chain. 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 chains to run in sequence. - `...`: currently no additional arguments ## Note Some of the underlying code used for generating random numbers from the Polya-Gamma distribution is taken from the `pgdraw` package written by Daniel F. Schmidt and Enes Makalic. Their code implements Algorithm 6 in PhD thesis of Jesse Bennett Windle (2013) [https://repositories.lib.utexas.edu/handle/2152/21842](https://repositories.lib.utexas.edu/handle/2152/21842). ## References 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. Bates, Douglas, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. tools:::Rd_expr_doi("10.18637/jss.v067.i01") . ## Author(s) Jeffrey W. Doser doserjef@msu.edu ## Returns An object of class `stIntPGOcc` that is a list comprised of: - **beta.samples**: a `coda` object of posterior samples for the occupancy regression coefficients. - **alpha.samples**: a `coda` object of posterior samples for the detection regression coefficients for all data sources. - **z.samples**: a three-dimensional array of posterior samples for the latent occupancy values, with dimensions corresponding to posterior sample, site, and primary time period. Note this object will contain predicted occupancy values for sites/primary time periods that were not sampled. - **psi.samples**: a three-dimensional array of posterior samples for the latent occupancy probability values, with dimensions corresponding to posterior sample, site, and primary time period. Note this object will contain predicted occupancy probabilities for sites/primary time periods that were not sampled. - **sigma.sq.psi.samples**: a `coda` object of posterior samples for variances of random intercepts included in the occupancy portion of the model. Only included if random intercepts are specified in `occ.formula`. - **sigma.sq.p.samples**: a `coda` object of posterior samples for variances of random intercpets included in the detection portion of the model. Includes random effect variances for all data sources. Only included if random intercepts are specified in `det.formula`. - **beta.star.samples**: a `coda` object of posterior samples for the occurrence random effects. Only included if random intercepts are specified in `occ.formula`. - **alpha.star.samples**: a `coda` object of posterior samples for the detection random effects in any of the data sources. Only included if random intercepts are specified in at least one of the individual data set detection formulas in `det.formula`. - **theta.samples**: a `coda` object of posterior samples for spatial covariance parameters and temporal covariance parameters if `ar1 = TRUE`. - **w.samples**: a `coda` object of posterior samples for latent spatial random effects. - **eta.samples**: a `coda` object of posterior samples for the AR(1) random effects for each primary time period. Only included if `ar1 = TRUE`. - **p.samples**: a list of four-dimensional arrays consisting of the posterior samples of detection probability for each data source. For each data source, the dimensions of the four-dimensional array correspond to MCMC sample, site, season, and replicate within season. - **like.samples**: a two-dimensional array of posterior samples for the likelihood values associated with each site and primary time period, for each individual data source. 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()`. The return object will include additional objects used for subsequent prediction and/or model fit evaluation. ## Examples ```r set.seed(332) # Simulate Data ----------------------------------------------------------- # Number of locations in each direction. This is the total region of interest # where some sites may or may not have a data source. J.x <- 15 J.y <- 15 J.all <- J.x * J.y # Number of data sources. n.data <- 3 # Sites for each data source. J.obs <- sample(ceiling(0.2 * J.all):ceiling(0.4 * J.all), n.data, replace = TRUE) # Maximum number of years for each data set n.time.max <- c(4, 8, 10) # Number of years each site in each data set is sampled n.time <- list() for (i in 1:n.data) { n.time[[i]] <- sample(1:n.time.max[i], J.obs[i], replace = TRUE) } # Replicates for each data source. n.rep <- list() for (i in 1:n.data) { n.rep[[i]] <- matrix(NA, J.obs[i], n.time.max[i]) for (j in 1:J.obs[i]) { n.rep[[i]][j, sample(1:n.time.max[i], n.time[[i]][j], replace = FALSE)] <- sample(1:4, n.time[[i]][j], replace = TRUE) } } # Total number of years across all data sets n.time.total <- 10 # List denoting the specific years each data set was sampled during. data.seasons <- list() for (i in 1:n.data) { data.seasons[[i]] <- sort(sample(1:n.time.total, n.time.max[i], replace = FALSE)) } # Occupancy covariates beta <- c(0, 0.4, 0.3) trend <- TRUE # Random occupancy covariates psi.RE <- list(levels = c(20), sigma.sq.psi = c(0.6)) p.occ <- length(beta) # Detection covariates alpha <- list() alpha[[1]] <- c(0, 0.2, -0.5) alpha[[2]] <- c(-1, 0.5, 0.3, -0.8) alpha[[3]] <- c(-0.5, 1) p.RE <- list() p.det.long <- sapply(alpha, length) p.det <- sum(p.det.long) # Spatial parameters sigma.sq <- 0.9 phi <- 3 / .5 # Simulate occupancy data. dat <- simTIntOcc(n.data = n.data, J.x = J.x, J.y = J.y, J.obs = J.obs, n.time = n.time, data.seasons = data.seasons, n.rep = n.rep, beta = beta, alpha = alpha, trend = trend, psi.RE = psi.RE, p.RE = p.RE, sp = TRUE, sigma.sq = sigma.sq, phi = phi, cov.model = 'exponential') y <- dat$y X <- dat$X.obs X.re <- dat$X.re.obs X.p <- dat$X.p sites <- dat$sites coords <- dat$coords.obs # Package all data into a list occ.covs <- list(trend = X[, , 2], occ.cov.1 = X[, , 3], occ.factor.1 = X.re[, , 1]) det.covs <- list() # Add covariates one by one det.covs[[1]] <- list(det.cov.1.1 = X.p[[1]][, , , 2], det.cov.1.2 = X.p[[1]][, , , 3]) det.covs[[2]] <- list(det.cov.2.1 = X.p[[2]][, , , 2], det.cov.2.2 = X.p[[2]][, , , 3], det.cov.2.3 = X.p[[2]][, , , 4]) det.covs[[3]] <- list(det.cov.3.1 = X.p[[3]][, , , 2]) data.list <- list(y = y, occ.covs = occ.covs, det.covs = det.covs, sites = sites, seasons = data.seasons, coords = coords) # Testing occ.formula <- ~ trend + occ.cov.1 + (1 | occ.factor.1) # Note that the names are not necessary. det.formula <- list(f.1 = ~ det.cov.1.1 + det.cov.1.2, f.2 = ~ det.cov.2.1 + det.cov.2.2 + det.cov.2.3, f.3 = ~ det.cov.3.1) # NOTE: this is a short run of the model, in reality we would run the # model for much longer. out <- stIntPGOcc(occ.formula = occ.formula, det.formula = det.formula, data = data.list, NNGP = TRUE, n.neighbors = 15, cov.model = 'exponential', n.batch = 3, batch.length = 25, n.report = 1, n.burn = 25, n.thin = 1, n.chains = 1) summary(out) ```

stIntPGOcc function