Function for prediction at new locations for multi-season multi-species spatially-varying coefficient occupancy models

The function predict collects posterior predictive samples for a set of new locations given an object of class `svcTMsPGOcc`. Prediction is possible for both the latent occupancy state as well as detection. Predictions are currently only possible for sampled primary time periods.

Usage

# S3 method for svcTMsPGOcc
predict(object, X.0, coords.0, t.cols, n.omp.threads = 1, 
                          verbose = TRUE, n.report = 100, 
                          ignore.RE = FALSE, type = 'occupancy', grid.index.0, ...)

Arguments

object: an object of class svcTMsPGOcc
X.0: the design matrix of covariates at the prediction locations. This should be a three-dimensional array, with dimensions corresponding to site, primary time period, and covariate, respectively. Note that the first covariate should consist of all 1s for the intercept if an intercept is included in the model. If random effects are included in the occupancy (or detection if type = 'detection') portion of the model, the levels of the random effects at the new locations/time periods should be included as an element of the three-dimensional array. The ordering of the levels should match the ordering used to fit the data in svcTMsPGOcc. The covariates should be organized in the same order as they were specified in the corresponding formula argument of svcTMsPGOcc. Names of the third dimension (covariates) of any random effects in X.0 must match the name of the random effects used to fit the model, if specified in the corresponding formula argument of svcTMsPGOcc. See example below.
coords.0: the spatial coordinates corresponding to X.0. Note that spOccupancy assumes coordinates are specified in a projected coordinate system.
t.cols: an indexing vector used to denote which primary time periods are contained in the design matrix of covariates at the prediction locations (X.0). The values should denote the specific primary time periods used to fit the model. The values should indicate the columns in data$y used to fit the model for which prediction is desired. See example below.
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, model specification and progress of the sampler is printed to the screen. Otherwise, nothing is printed to the screen.
ignore.RE: logical value that specifies whether or not to remove random unstructured occurrence (or detection if type = 'detection') effects from the subsequent predictions. If TRUE, random effects will be included. If FALSE, unstructured random effects will be set to 0 and predictions will only be generated from the fixed effects, the spatial random effects, and AR(1) random effects if the model was fit with ar1 = TRUE.
n.report: the interval to report sampling progress.
type: a quoted keyword indicating what type of prediction to produce. Valid keywords are 'occupancy' to predict latent occupancy probability and latent occupancy values (this is the default), or 'detection' to predict detection probability given new values of detection covariates.
grid.index.0: an indexing vector used to specify how each row in X.0 corresponds to the coordinates specified in coords.0. Only relevant if the spatial random effect was estimated at a higher spatial resolution (e.g., grid cells) than point locations.
...: currently no additional arguments

Note

When ignore.RE = FALSE, both sampled levels and non-sampled levels of unstructured random effects are supported for prediction. For sampled levels, the posterior distribution for the random intercept corresponding to that level of the random effect will be used in the prediction. For non-sampled levels, random values are drawn from a normal distribution using the posterior samples of the random effect variance, which results in fully propagated uncertainty in predictions with models that incorporate random effects.

Occurrence predictions at sites that are only sampled for a subset of the total number of primary time periods are obtained directly when fitting the model. See the psi.samples and z.samples portions of the output list from the model object of class svcTMsPGOcc.

Author

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

Value

A list object of class predict.svcTMsPGOcc. When type = 'occupancy', the list consists of:

psi.0.samples: a four-dimensional object of posterior predictive samples for the latent occupancy probability values with dimensions corresponding to posterior predictive sample, species, site, and primary time period.
z.0.samples: a three-dimensional object of posterior predictive samples for the latent occupancy values with dimensions corresponding to posterior predictive sample, species, site, and primary time period.
w.0.samples: a four-dimensional array of posterior predictive samples for the latent spatial factors with dimensions correpsonding to MCMC sample, latent factor, site, and spatially-varying coefficient.

When type = 'detection', the list consists of:

p.0.samples: a four-dimensional object of posterior predictive samples for the detection probability values with dimensions corresponding to posterior predictive sample, site, and primary time period.

The return object will include additional objects used for standard extractor functions.

Examples

# Simulate Data -----------------------------------------------------------
set.seed(500)
J.x <- 8
J.y <- 8
J <- J.x * J.y
# Years sampled
n.time <- sample(3:10, J, replace = TRUE)
# n.time <- rep(10, J)
n.time.max <- max(n.time)
# Replicates
n.rep <- matrix(NA, J, max(n.time))
for (j in 1:J) {
  n.rep[j, 1:n.time[j]] <- sample(2:4, n.time[j], replace = TRUE)
  # n.rep[j, 1:n.time[j]] <- rep(4, n.time[j])
}
N <- 7
# Community-level covariate effects
# Occurrence
beta.mean <- c(-3, -0.2, 0.5)
trend <- FALSE
sp.only <- 0
p.occ <- length(beta.mean)
tau.sq.beta <- c(0.6, 1.5, 1.4)
# Detection
alpha.mean <- c(0, 1.2, -1.5)
tau.sq.alpha <- c(1, 0.5, 2.3)
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]))
}
sp <- TRUE
svc.cols <- c(1, 2)
p.svc <- length(svc.cols)
n.factors <- 2
phi <- runif(p.svc * n.factors, 3 / .9, 3 / .3)
factor.model <- TRUE
cov.model <- 'exponential'
ar1 <- TRUE
sigma.sq.t <- runif(N, 0.05, 1)
rho <- runif(N, 0.1, 1)

dat <- simTMsOcc(J.x = J.x, J.y = J.y, n.time = n.time, n.rep = n.rep, N = N,
     beta = beta, alpha = alpha, sp.only = sp.only, trend = trend,
     psi.RE = psi.RE, p.RE = p.RE, factor.model = factor.model,
                 svc.cols = svc.cols, n.factors = n.factors, phi = phi, sp = sp,
                 cov.model = cov.model, ar1 = ar1, sigma.sq.t = sigma.sq.t, rho = rho)

# Subset data for prediction
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]
# Prediction covariates
X.0 <- dat$X[pred.indx, , , drop = FALSE]
# Detection covariates
X.p <- dat$X.p[-pred.indx, , , , drop = FALSE]
# Coordinates
coords <- dat$coords[-pred.indx, ]
coords.0 <- dat$coords[pred.indx, ]

occ.covs <- list(occ.cov.1 = X[, , 2],
     occ.cov.2 = X[, , 3])
det.covs <- list(det.cov.1 = X.p[, , , 2],
     det.cov.2 = X.p[, , , 3])

data.list <- list(y = y, occ.covs = occ.covs,
                  det.covs = det.covs,
                  coords = coords)
# Priors
prior.list <- list(beta.comm.normal = list(mean = 0, var = 2.72),
       alpha.comm.normal = list(mean = 0, var = 2.72),
       tau.sq.beta.ig = list(a = 0.1, b = 0.1),
       tau.sq.alpha.ig = list(a = 0.1, b = 0.1),
       rho.unif = list(a = -1, b = 1),
       sigma.sq.t.ig = list(a = 0.1, b = 0.1),
                   phi.unif = list(a = 3 / .9, b = 3 / .1))
z.init <- apply(y, c(1, 2, 3), function(a) as.numeric(sum(a, na.rm = TRUE) > 0))
inits.list <- list(alpha.comm = 0, beta.comm = 0, beta = 0,
       alpha = 0, tau.sq.beta = 1, tau.sq.alpha = 1,
       rho = 0.5, sigma.sq.t = 0.5,
       phi = 3 / .5, z = z.init)
# Tuning
tuning.list <- list(phi = 1, rho = 0.5)

# Number of batches
n.batch <- 5
# Batch length
batch.length <- 25
n.burn <- 25
n.thin <- 1
n.samples <- n.batch * batch.length

out <- svcTMsPGOcc(occ.formula = ~ occ.cov.1 + occ.cov.2,
                det.formula = ~ det.cov.1 + det.cov.2,
                data = data.list,
                inits = inits.list,
                n.batch = n.batch,
                batch.length = batch.length,
                accept.rate = 0.43,
    ar1 = TRUE,
    svc.cols = svc.cols,
    NNGP = TRUE,
    n.neighbors = 5,
    n.factors = n.factors,
    cov.model = 'exponential',
                priors = prior.list,
                tuning = tuning.list,
                n.omp.threads = 1,
                verbose = TRUE,
                n.report = 1,
                n.burn = n.burn,
    n.thin = n.thin,
    n.chains = 1)
#> ----------------------------------------
#> 	Preparing to run the model
#> ----------------------------------------
#> lambda is not specified in initial values.
#> Setting initial values of the lower triangle to 0
#> ----------------------------------------
#> 	Building the neighbor list
#> ----------------------------------------
#> ----------------------------------------
#> Building the neighbors of neighbors list
#> ----------------------------------------
#> ----------------------------------------
#> 	Model description
#> ----------------------------------------
#> Spatial Factor NNGP Multi-season Multi-species Occupancy Model with Polya-Gamma latent
#> variables with 48 sites, 7 species, and 10 primary time periods.
#> 
#> Samples per chain: 125 (5 batches of length 25)
#> Burn-in: 25 
#> Thinning Rate: 1 
#> Number of Chains: 1 
#> Total Posterior Samples: 100 
#> 
#> Number of spatially-varying coefficients: 2 
#> Using the exponential spatial correlation model.
#> 
#> Using 2 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: 1 of 5, 20.00%
#> 	Coefficient	Latent Factor	Acceptance	Tuning
#> 	1		1		76.0		1.02020
#> 	1		2		68.0		1.02020
#> 	2		1		76.0		1.02020
#> 	2		2		68.0		1.02020
#> 	Species		Parameter	Acceptance	Tuning
#> 	1		rho		68.0		0.51010
#> 	2		rho		64.0		0.51010
#> 	3		rho		88.0		0.51010
#> 	4		rho		68.0		0.51010
#> 	5		rho		76.0		0.51010
#> 	6		rho		88.0		0.51010
#> 	7		rho		80.0		0.51010
#> -------------------------------------------------
#> Batch: 2 of 5, 40.00%
#> 	Coefficient	Latent Factor	Acceptance	Tuning
#> 	1		1		56.0		1.03045
#> 	1		2		72.0		1.03045
#> 	2		1		68.0		1.03045
#> 	2		2		84.0		1.03045
#> 	Species		Parameter	Acceptance	Tuning
#> 	1		rho		72.0		0.51523
#> 	2		rho		72.0		0.51523
#> 	3		rho		68.0		0.51523
#> 	4		rho		72.0		0.51523
#> 	5		rho		88.0		0.51523
#> 	6		rho		68.0		0.51523
#> 	7		rho		76.0		0.51523
#> -------------------------------------------------
#> Batch: 3 of 5, 60.00%
#> 	Coefficient	Latent Factor	Acceptance	Tuning
#> 	1		1		68.0		1.04081
#> 	1		2		52.0		1.04081
#> 	2		1		84.0		1.04081
#> 	2		2		60.0		1.04081
#> 	Species		Parameter	Acceptance	Tuning
#> 	1		rho		88.0		0.52041
#> 	2		rho		84.0		0.52041
#> 	3		rho		80.0		0.52041
#> 	4		rho		68.0		0.52041
#> 	5		rho		64.0		0.52041
#> 	6		rho		76.0		0.52041
#> 	7		rho		76.0		0.52041
#> -------------------------------------------------
#> Batch: 4 of 5, 80.00%
#> 	Coefficient	Latent Factor	Acceptance	Tuning
#> 	1		1		52.0		1.05127
#> 	1		2		76.0		1.05127
#> 	2		1		68.0		1.05127
#> 	2		2		64.0		1.05127
#> 	Species		Parameter	Acceptance	Tuning
#> 	1		rho		76.0		0.52564
#> 	2		rho		72.0		0.52564
#> 	3		rho		80.0		0.52564
#> 	4		rho		84.0		0.52564
#> 	5		rho		88.0		0.52564
#> 	6		rho		44.0		0.52564
#> 	7		rho		72.0		0.52564
#> -------------------------------------------------
#> Batch: 5 of 5, 100.00%

summary(out)
#> 
#> Call:
#> svcTMsPGOcc(occ.formula = ~occ.cov.1 + occ.cov.2, det.formula = ~det.cov.1 + 
#>     det.cov.2, data = data.list, inits = inits.list, priors = prior.list, 
#>     tuning = tuning.list, svc.cols = svc.cols, cov.model = "exponential", 
#>     NNGP = TRUE, n.neighbors = 5, n.factors = n.factors, n.batch = n.batch, 
#>     batch.length = batch.length, accept.rate = 0.43, n.omp.threads = 1, 
#>     verbose = TRUE, ar1 = TRUE, n.report = 1, n.burn = n.burn, 
#>     n.thin = n.thin, n.chains = 1)
#> 
#> Samples per Chain: 125
#> Burn-in: 25
#> Thinning Rate: 1
#> Number of Chains: 1
#> Total Posterior Samples: 100
#> Run Time (min): 0.0088
#> 
#> ----------------------------------------
#> 	Community Level
#> ----------------------------------------
#> Occurrence Means (logit scale): 
#>                Mean     SD    2.5%     50%   97.5% Rhat ESS
#> (Intercept) -3.3761 0.3495 -3.9395 -3.4174 -2.5106   NA  15
#> occ.cov.1    0.4690 0.2859 -0.0675  0.4622  0.9628   NA  36
#> occ.cov.2    0.8956 0.5736 -0.1061  0.9120  1.8918   NA  69
#> 
#> Occurrence Variances (logit scale): 
#>               Mean     SD   2.5%    50%  97.5% Rhat ESS
#> (Intercept) 0.5856 0.7889 0.0433 0.3317 3.4290   NA  16
#> occ.cov.1   0.6197 0.4174 0.1596 0.5230 1.6035   NA  35
#> occ.cov.2   3.1358 2.4545 0.7807 2.3574 9.4179   NA  57
#> 
#> Detection Means (logit scale): 
#>                Mean     SD    2.5%     50%  97.5% Rhat ESS
#> (Intercept) -0.0442 0.2010 -0.3538 -0.0434 0.3813   NA  44
#> det.cov.1    1.2213 0.3704  0.4155  1.2985 1.7098   NA 100
#> det.cov.2   -1.3102 0.7133 -2.8578 -1.2279 0.1591   NA  50
#> 
#> Detection Variances (logit scale): 
#>               Mean     SD   2.5%    50%   97.5% Rhat ESS
#> (Intercept) 0.2050 0.2347 0.0401 0.1447  0.9314   NA  28
#> det.cov.1   1.3907 1.2551 0.2820 0.8844  5.0474   NA  17
#> det.cov.2   3.3405 4.7226 0.4156 1.5539 18.7433   NA  34
#> 
#> ----------------------------------------
#> 	Species Level
#> ----------------------------------------
#> Occurrence (logit scale): 
#>                    Mean     SD    2.5%     50%   97.5% Rhat ESS
#> (Intercept)-sp1 -3.8583 0.6182 -5.4566 -3.7876 -2.6966   NA   5
#> (Intercept)-sp2 -3.3938 0.3712 -4.1478 -3.3665 -2.7860   NA   9
#> (Intercept)-sp3 -3.6292 0.4360 -4.2323 -3.7231 -2.5083   NA   5
#> (Intercept)-sp4 -2.8321 0.6465 -3.7498 -3.0934 -1.7281   NA   3
#> (Intercept)-sp5 -3.4121 0.4382 -4.4794 -3.3413 -2.8393   NA   9
#> (Intercept)-sp6 -3.4963 0.3439 -4.1920 -3.4940 -2.9282   NA  13
#> (Intercept)-sp7 -3.6024 0.4333 -4.5418 -3.5582 -2.8465   NA   7
#> occ.cov.1-sp1    0.0136 0.3063 -0.6171  0.0304  0.5464   NA  13
#> occ.cov.1-sp2    0.1304 0.3693 -0.6035  0.2082  0.8206   NA   9
#> occ.cov.1-sp3    0.0843 0.3194 -0.5299  0.0691  0.6640   NA  15
#> occ.cov.1-sp4    0.0965 0.3168 -0.6872  0.1075  0.6357   NA  13
#> occ.cov.1-sp5    1.2095 0.2465  0.8139  1.1824  1.6849   NA  21
#> occ.cov.1-sp6    0.5881 0.3255 -0.0008  0.6512  1.1053   NA  14
#> occ.cov.1-sp7    1.4706 0.5148  0.3582  1.5620  2.2937   NA   6
#> occ.cov.2-sp1    1.3003 0.3995  0.7061  1.2369  2.2593   NA  22
#> occ.cov.2-sp2    2.0746 0.2777  1.4854  2.0884  2.6098   NA  21
#> occ.cov.2-sp3   -0.7888 0.2888 -1.3396 -0.8040 -0.2573   NA  27
#> occ.cov.2-sp4   -0.5775 0.2720 -1.1319 -0.5895 -0.0910   NA  26
#> occ.cov.2-sp5    0.3411 0.3021 -0.3524  0.3589  0.8913   NA  18
#> occ.cov.2-sp6    2.0080 0.3373  1.4592  1.9585  2.6483   NA  15
#> occ.cov.2-sp7    2.9260 0.5167  1.9931  2.9511  3.8630   NA   7
#> 
#> Detection (logit scale): 
#>                    Mean     SD    2.5%     50%   97.5% Rhat ESS
#> (Intercept)-sp1  0.1990 0.3355 -0.4692  0.1975  0.8135   NA  40
#> (Intercept)-sp2 -0.1769 0.2182 -0.5209 -0.1908  0.2722   NA  48
#> (Intercept)-sp3 -0.0646 0.2966 -0.6354 -0.0698  0.4500   NA  43
#> (Intercept)-sp4  0.0584 0.1911 -0.2766  0.0422  0.4161   NA  69
#> (Intercept)-sp5  0.1424 0.2742 -0.3112  0.1234  0.6840   NA  51
#> (Intercept)-sp6  0.2229 0.2610 -0.2750  0.2274  0.7493   NA  39
#> (Intercept)-sp7 -0.3849 0.2816 -0.9896 -0.3538  0.0497   NA  15
#> det.cov.1-sp1    2.9319 0.6738  1.9426  2.8806  4.4151   NA  19
#> det.cov.1-sp2    0.9748 0.2670  0.4865  0.9911  1.4329   NA  38
#> det.cov.1-sp3    0.1007 0.6389 -1.1233  0.0764  1.2339   NA  31
#> det.cov.1-sp4    0.5407 0.2136  0.0827  0.5561  0.9364   NA  36
#> det.cov.1-sp5    1.8469 0.3910  1.1431  1.8686  2.5647   NA  35
#> det.cov.1-sp6    1.5676 0.3387  0.9389  1.5626  2.2115   NA  29
#> det.cov.1-sp7    1.3889 0.3075  0.8150  1.3872  1.9883   NA  27
#> det.cov.2-sp1   -0.6511 0.4771 -1.6769 -0.5932  0.0906   NA  38
#> det.cov.2-sp2   -0.2977 0.2000 -0.6930 -0.2750  0.0731   NA  90
#> det.cov.2-sp3   -4.2877 1.9126 -9.0545 -3.4591 -2.2855   NA   3
#> det.cov.2-sp4   -1.4425 0.2744 -1.9622 -1.4475 -0.9743   NA  24
#> det.cov.2-sp5   -0.7265 0.3065 -1.4205 -0.7070 -0.1790   NA  30
#> det.cov.2-sp6   -2.1308 0.4312 -2.8834 -2.1789 -1.3725   NA  22
#> det.cov.2-sp7   -1.5891 0.3519 -2.3017 -1.5600 -0.9656   NA  23
#> 
#> ----------------------------------------
#> 	Spatio-temporal Covariance: 
#> ----------------------------------------
#>                      Mean     SD    2.5%     50%   97.5% Rhat ESS
#> phi-1-(Intercept) 10.6827 4.8640  4.7713  8.5566 19.3922   NA  11
#> phi-2-(Intercept) 21.5459 5.5072 10.9718 22.6073 29.4404   NA  10
#> phi-1-occ.cov.1   15.7870 6.8645  4.1130 16.4164 27.0621   NA   7
#> phi-2-occ.cov.1   15.0191 5.3840  5.7722 14.5669 25.1125   NA  16
#> sigma.sq.t-sp1     1.1013 1.7352  0.0420  0.5099  4.4786   NA  20
#> sigma.sq.t-sp2     0.3148 0.3147  0.0457  0.1987  1.3781   NA  19
#> sigma.sq.t-sp3     0.8575 1.2088  0.0498  0.2828  5.0274   NA  10
#> sigma.sq.t-sp4     5.0118 4.5068  1.1761  3.5892 17.7620   NA  16
#> sigma.sq.t-sp5     0.8914 0.7928  0.0580  0.7110  2.4268   NA  20
#> sigma.sq.t-sp6     0.6292 0.7769  0.0600  0.3621  2.8410   NA  29
#> sigma.sq.t-sp7     2.9082 2.4520  0.2760  1.9393 10.0092   NA  17
#> rho-sp1           -0.0029 0.4855 -0.8339  0.0752  0.7324   NA   3
#> rho-sp2            0.6561 0.2960 -0.0020  0.8112  0.9537   NA   4
#> rho-sp3            0.3865 0.4240 -0.3296  0.4883  0.9428   NA   4
#> rho-sp4           -0.2984 0.3342 -0.7428 -0.3578  0.4534   NA   7
#> rho-sp5            0.3237 0.4132 -0.4309  0.3327  0.9617   NA   6
#> rho-sp6           -0.5344 0.4160 -0.9690 -0.6282  0.2190   NA   2
#> rho-sp7            0.2590 0.4829 -0.5486  0.3330  0.9142   NA   2

# Predict at new sites across all n.max.years
# Take a look at array of covariates for prediction
str(X.0)
#>  num [1:16, 1:10, 1:3] 1 1 1 1 1 1 1 1 1 1 ...
# Subset to only grab time periods 1, 2, and 5
t.cols <- c(1, 2, 5)
X.pred <- X.0[, t.cols, ]
out.pred <- predict(out, X.pred, coords.0, t.cols = t.cols, type = 'occupancy')
#> ----------------------------------------
#> 	Prediction description
#> ----------------------------------------
#> Spatial Factor NNGP Multi-season, Multi-species Occupancy model with Polya-Gamma latent
#> variable fit with 48 sites and 3 years.
#> 
#> Number of covariates 3 (including intercept if specified).
#> 
#> Number of spatially-varying covariates 2 (including intercept if specified).
#> 
#> Using the exponential spatial correlation model.
#> 
#> Using 5 nearest neighbors.
#> Using 2 latent spatial factors.
#> 
#> Number of MCMC samples 100.
#> 
#> Predicting at 16 non-sampled locations.
#> 
#> 
#> Source compiled with OpenMP support and model fit using 1 threads.
#> -------------------------------------------------
#> 		Predicting
#> -------------------------------------------------
#> Location: 16 of 16, 100.00%
#> Generating latent occupancy state
str(out.pred)
#> List of 6
#>  $ z.0.samples  : num [1:100, 1:7, 1:16, 1:3] 0 0 0 0 0 0 0 0 0 0 ...
#>  $ w.0.samples  : num [1:100, 1:2, 1:16, 1:2] 0.985 -1.16 1.352 -0.166 0.328 ...
#>  $ psi.0.samples: num [1:100, 1:7, 1:16, 1:3] 0.04339 0.00533 0.17248 0.08771 0.13242 ...
#>  $ run.time     : 'proc_time' Named num [1:5] 0.025 0.068 0.019 0 0
#>   ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ...
#>  $ call         : language predict.svcTMsPGOcc(object = out, X.0 = X.pred, coords.0 = coords.0, t.cols = t.cols,      type = "occupancy")
#>  $ object.class : chr "svcTMsPGOcc"
#>  - attr(*, "class")= chr "predict.svcTMsPGOcc"

# Extract SVC samples for each species at prediction locations
svc.samples <- getSVCSamples(out, out.pred)
str(svc.samples)
#> List of 2
#>  $ (Intercept): num [1:100, 1:7, 1:16] -1.75 -4.07 -1.22 -2.77 -2.57 ...
#>  $ occ.cov.1  : num [1:100, 1:7, 1:16] 1.099 1.85 0.425 -0.395 -1.322 ...