# Function for Fitting a Spatial Factor Joint Species Distribution Model

`sfJSDM.Rd`

The function `sfJSDM`

fits a spatially-explicit joint species distribution model. This model does not explicitly account for imperfect detection (see `sfMsPGOcc()`

). We use Polya-Gamma latent variables and a spatial factor modeling approach. Currently, models are implemented using a Nearest Neighbor Gaussian Process.

## Usage

```
sfJSDM(formula, data, inits, priors, tuning,
cov.model = 'exponential', NNGP = TRUE,
n.neighbors = 15, search.type = 'cb',
std.by.sp = FALSE, n.factors, n.batch,
batch.length, accept.rate = 0.43, n.omp.threads = 1,
verbose = TRUE, 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, monitors, keep.only.mean.95, shared.spatial = FALSE, ...)
```

## 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 are allowed using lme4 syntax (Bates et al. 2015).

- data
a list containing data necessary for model fitting. Valid tags are

`y`

,`covs`

,`coords`

,`range.ind`

.`y`

is a two-dimensional array with first dimension equal to the number of species and second dimension equal to the number of sites. Note how this differs from other`spOccupancy`

functions in that`y`

does not have any replicate surveys. This is because`sfJSDM`

does not account for imperfect detection.`covs`

is a matrix or data frame containing the variables used in the model, with \(J\) rows for each column (variable).`coords`

is a matrix with \(J\) rows and 2 columns consisting of the spatial coordinates of each site in the data. Note that`spOccupancy`

assumes coordinates are specified in a projected coordinate system.`range.ind`

is a matrix with rows corresponding to species and columns corresponding to sites, with each element taking value 1 if that site is within the range of the corresponding species and 0 if it is outside of the range. This matrix is not required, but it can be helpful to restrict the modeled area for each individual species to be within the realistic range of locations for that species when estimating the model parameters.- inits
a list with each tag corresponding to a parameter name. Valid tags are

`beta.comm`

,`beta`

,`tau.sq.beta`

,`phi`

,`lambda`

,`sigma.sq.psi`

, and`nu`

.`nu`

is only specified if`cov.model = "matern"`

.`sigma.sq.psi`

is only specified if random intercepts are included in`formula`

. 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.comm.normal`

,`tau.sq.beta.ig`

,`phi.unif`

,`nu.unif`

, and`sigma.sq.psi.ig`

. Community-level occurrence (`beta.comm`

) regression coefficients 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. Community-level variance parameters (`tau.sq.beta`

) 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, which are each specified as vectors of length equal to the number of coefficients to be estimated or a single value if priors are the same for all parameters. If not specified, prior shape and scale parameters are set to 0.1. The spatial factor model fits`n.factors`

independent spatial processes. The spatial decay`phi`

and smoothness`nu`

parameters for each latent factor are assumed to follow Uniform distributions. The hyperparameters of the Uniform are passed as a list with two elements, with both elements being vectors of length`n.factors`

corresponding to the lower and upper support, respectively, or as a single value if the same value is assigned for all factors. The priors for the factor loadings matrix`lambda`

are fixed following the standard spatial factor model to ensure parameter identifiability (Christensen and Amemlya 2002). The upper triangular elements of the`N x n.factors`

matrix are fixed at 0 and the diagonal elements are fixed at 1. The lower triangular elements are assigned a standard normal prior (i.e., mean 0 and variance 1).`sigma.sq.psi`

is the random effect variance for any random effects, and is 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.- tuning
a list with each tag corresponding to a parameter name. Valid tags are

`phi`

and`nu`

. The value portion of each tag defines the initial variance of the adaptive sampler. We assume the initial variance of the adaptive sampler is the same for each species, although the adaptive sampler will adjust the tuning variances separately for each species. 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. See Datta et al. (2016) and Finley et al. (2019) for more information. For spatial factor models, only`NNGP = TRUE`

is currently 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 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.- std.by.sp
a logical value indicating whether the covariates are standardized separately for each species within the corresponding range for each species (

`TRUE`

) or not (`FALSE`

). Note that if`range.ind`

is specified in`data.list`

, this will result in the covariates being standardized differently for each species based on the sites where`range.ind == 1`

for that given species. If`range.ind`

is not specified and`std.by.sp = TRUE`

, this will simply be equivalent to standardizing the covariates across all locations prior to fitting the model.- n.factors
the number of factors to use in the spatial factor model approach. Typically, the number of factors is set to be small (e.g., 4-5) relative to the total number of species in the community, which will lead to substantial decreases in computation time. However, the value can be anywhere between 1 and N (the number of species in the community).

- 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 to run for the Adaptive MCMC sampler. See Roberts and Rosenthal (2009) for details.

- accept.rate
target acceptance rate for Adaptive MCMC. Defaul 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.- n.report
the interval to report Metropolis sampler acceptance and MCMC progress. Note this is specified in terms of batches and not overall samples for spatial models.

- 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.

- 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). 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`

.- monitors
a character vector used to indicate if only a subset of the model model parameters are desired to be monitored. If posterior samples of all parameters are desired, then don't specify the argument (this is the default). When working with a large number of species and/or sites, the full model object can be quite large, and so this argument can be used to only return samples of specific parameters to help reduce the size of this resulting object. Valid tags include

`beta.comm`

,`tau.sq.beta`

,`beta`

,`z`

,`psi`

,`lambda`

,`theta`

,`w`

,`like`

(used for WAIC calculation),`beta.star`

,`sigma.sq.psi`

. Note that if all parameters are not returned, subsequent functions that require the model object may not work. We only recommend specifying this option when working with large data sets (e.g., > 100 species and/or > 10,000 sites).- keep.only.mean.95
not currently supported.

- shared.spatial
a logical value used to specify whether a common spatial process should be estimated for all species instead of the factor modeling approach. If true, a spatial variance parameter

`sigma.sq`

is estimated for the model, which can be specified in the initial values and prior distributions (`sigma.sq.ig`

).- ...
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.

## References

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., Datta, A., and Banerjee, S. (2020). spNNGP R package
for nearest neighbor Gaussian process models. *arXiv* preprint arXiv:2001.09111.

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.

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 .

Hooten, M. B., and Hobbs, N. T. (2015). A guide to Bayesian model
selection for ecologists. *Ecological Monographs*, 85(1), 3-28.

Christensen, W. F., and Amemiya, Y. (2002). Latent variable analysis
of multivariate spatial data. *Journal of the American Statistical Association*,
97(457), 302-317.

## Author

Jeffrey W. Doser doserjef@msu.edu,

Andrew O. Finley finleya@msu.edu

## Value

An object of class `sfJSDM`

that is a list comprised of:

- beta.comm.samples
a

`coda`

object of posterior samples for the community level occurrence regression coefficients.- tau.sq.beta.samples
a

`coda`

object of posterior samples for the occurrence community variance parameters.- beta.samples
a

`coda`

object of posterior samples for the species level occurrence regression coefficients.- theta.samples
a

`coda`

object of posterior samples for the species level correlation parameters.- lambda.samples
a

`coda`

object of posterior samples for the latent spatial factor loadings.- psi.samples
a three-dimensional array of posterior samples for the latent occurrence probability values for each species.

- w.samples
a three-dimensional array of posterior samples for the latent spatial random effects for each latent factor. Array dimensions correspond to MCMC sample, latent factor, and site. If

`shared.spatial = TRUE`

, this is still returned as a three-dimensional array where the first dimension is MCMC sample, second dimension is 1, and third dimension is site.- sigma.sq.psi.samples
a

`coda`

object of posterior samples for variances of random intercepts included in the occurrence portion of the model. Only included if random intercepts are specified in`formula`

.- beta.star.samples
a

`coda`

object of posterior samples for the occurrence random effects. Only included if random intercepts are specified in`formula`

.- like.samples
a three-dimensional array of posterior samples for the likelihood value associated with each site and species. 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
MCMC sampler execution time reported using

`proc.time()`

.- k.fold.deviance
vector of scoring rules (deviance) from k-fold cross-validation. A separate value is reported for each species. 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 detection probability
estimated values are not included in the model object, but can be extracted
using `fitted()`

.

## Examples

```
J.x <- 8
J.y <- 8
J <- J.x * J.y
n.rep<- sample(2:4, size = J, replace = TRUE)
N <- 6
# Community-level covariate effects
# Occurrence
beta.mean <- c(0.2)
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6)
# Detection
alpha.mean <- c(0)
tau.sq.alpha <- c(1)
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]))
}
alpha.true <- alpha
n.factors <- 3
phi <- rep(3 / .7, n.factors)
sigma.sq <- rep(2, n.factors)
nu <- rep(2, n.factors)
dat <- simMsOcc(J.x = J.x, J.y = J.y, n.rep = n.rep, N = N, beta = beta, alpha = alpha,
psi.RE = psi.RE, p.RE = p.RE, sp = TRUE, sigma.sq = sigma.sq,
phi = phi, nu = nu, cov.model = 'matern', factor.model = TRUE,
n.factors = n.factors)
#> sigma.sq is specified but will be set to 1 for spatial latent factor model
pred.indx <- sample(1:J, round(J * .25), replace = FALSE)
y <- dat$y[, -pred.indx, , drop = FALSE]
# Occupancy covariates
X <- dat$X[-pred.indx, , drop = FALSE]
coords <- as.matrix(dat$coords[-pred.indx, , drop = FALSE])
# Prediction covariates
X.0 <- dat$X[pred.indx, , drop = FALSE]
coords.0 <- as.matrix(dat$coords[pred.indx, , drop = FALSE])
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , drop = FALSE]
y <- apply(y, c(1, 2), max, na.rm = TRUE)
data.list <- list(y = y, coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
nu.unif = list(0.5, 2.5))
# Starting values
inits.list <- list(beta.comm = 0,
beta = 0,
fix = TRUE,
tau.sq.beta = 1)
# Tuning
tuning.list <- list(phi = 1, nu = 0.25)
batch.length <- 25
n.batch <- 5
n.report <- 100
formula <- ~ 1
out <- sfJSDM(formula = formula,
data = data.list,
inits = inits.list,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = prior.list,
cov.model = "matern",
tuning = tuning.list,
n.factors = 3,
n.omp.threads = 1,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
search.type = 'cb',
n.report = 10,
n.burn = 0,
n.thin = 1,
n.chains = 2)
#> ----------------------------------------
#> Preparing to run the model
#> ----------------------------------------
#> covariates (covs) not specified in data.
#> Assuming intercept only model.
#> No prior specified for phi.unif.
#> Setting uniform bounds based on the range of observed spatial coordinates.
#> phi is not specified in initial values.
#> Setting initial value to random values from the prior distribution
#> lambda is not specified in initial values.
#> Setting initial values of the lower triangle to 0
#> nu is not specified in initial values.
#> Setting initial values to random values from the prior distribution
#> w is not specified in initial values.
#> Setting initial value to 0
#> Fixing initial values across all chains
#> ----------------------------------------
#> Building the neighbor list
#> ----------------------------------------
#> ----------------------------------------
#> Building the neighbors of neighbors list
#> ----------------------------------------
#> ----------------------------------------
#> Model description
#> ----------------------------------------
#> Spatial Factor NNGP JSDM with Polya-Gamma latent
#> variable fit with 48 sites and 6 species.
#>
#> Samples per chain: 125 (5 batches of length 25)
#> Burn-in: 0
#> Thinning Rate: 1
#> Number of Chains: 2
#> Total Posterior Samples: 250
#>
#> Using the matern spatial correlation model.
#>
#> Using 3 latent spatial factors.
#> Using 5 nearest neighbors.
#>
#> Source compiled with OpenMP support and model fit using 1 thread(s).
#>
#> Adaptive Metropolis with target acceptance rate: 43.0
#> ----------------------------------------
#> Chain 1
#> ----------------------------------------
#> Sampling ...
#> Batch: 5 of 5, 100.00%
#> ----------------------------------------
#> Chain 2
#> ----------------------------------------
#> Sampling ...
#> Batch: 5 of 5, 100.00%
summary(out)
#>
#> Call:
#> sfJSDM(formula = formula, data = data.list, inits = inits.list,
#> priors = prior.list, tuning = tuning.list, cov.model = "matern",
#> NNGP = TRUE, n.neighbors = 5, search.type = "cb", n.factors = 3,
#> n.batch = n.batch, batch.length = batch.length, accept.rate = 0.43,
#> n.omp.threads = 1, verbose = TRUE, n.report = 10, n.burn = 0,
#> n.thin = 1, n.chains = 2)
#>
#> Samples per Chain: 125
#> Burn-in: 0
#> Thinning Rate: 1
#> Number of Chains: 2
#> Total Posterior Samples: 250
#> Run Time (min): 0.0219
#>
#> ----------------------------------------
#> Community Level
#> ----------------------------------------
#> Means (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) -0.9425 0.6909 -2.3163 -0.9389 0.3925 1.0301 250
#>
#> Variances (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept) 4.2243 6.6792 0.6289 2.8683 13.0285 1.6847 250
#>
#> ----------------------------------------
#> Species Level
#> ----------------------------------------
#> Estimates (logit scale):
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> (Intercept)-sp1 -0.7497 0.5126 -1.9118 -0.7306 0.1198 2.1432 19
#> (Intercept)-sp2 1.0455 0.5340 0.0193 1.0149 2.0767 1.3089 19
#> (Intercept)-sp3 0.1149 0.5661 -1.1997 0.1519 1.0835 1.2509 55
#> (Intercept)-sp4 -1.4247 0.5219 -2.4942 -1.3843 -0.4766 1.4222 61
#> (Intercept)-sp5 -3.6508 0.9878 -5.8141 -3.5509 -2.1360 1.9658 23
#> (Intercept)-sp6 -1.9058 0.5387 -3.0428 -1.8938 -0.9686 1.2109 60
#>
#> ----------------------------------------
#> Spatial Covariance
#> ----------------------------------------
#> Mean SD 2.5% 50% 97.5% Rhat ESS
#> phi-1 10.9066 4.4195 6.0673 9.5505 20.9051 8.2874 5
#> phi-2 16.5139 3.6911 10.2840 17.2128 20.9936 1.0491 7
#> phi-3 14.5076 4.1317 6.4794 15.3228 20.2422 2.2817 8
#> nu-1 1.7018 0.2489 1.2593 1.6679 2.1605 1.0012 6
#> nu-2 1.8207 0.3847 1.2394 1.7756 2.4006 1.6481 4
#> nu-3 2.1162 0.2544 1.5330 2.1884 2.4179 1.3254 4
```