Spatial and spatiotemporal GLMMs with TMB
sdmTMB is an R package that fits spatial and spatiotemporal predictiveprocess GLMMs (Generalized Linear Mixed Effects Models) using Template Model Builder (TMB), RINLA, and Gaussian Markov random fields. One common application is for species distribution models (SDMs). See also the documentation site.
Table of contents
 Installation
 Overview
 Getting help
 Citation
 Related software
 Basic use

Advanced functionality
 Timevarying coefficients
 Spatially varying coefficients (SVC)
 Random intercepts
 Breakpoint and threshold effects
 Simulating data
 Sampling from the joint precision matrix
 Calculating uncertainty on spatial predictions
 Cross validation
 Priors
 Bayesian MCMC sampling with Stan
 Turning off random fields
 Using a custom INLA mesh
 Barrier meshes
Installation
sdmTMB can be installed from CRAN:
install.packages("sdmTMB", dependencies = TRUE)
Assuming you have a C++ compiler installed, the development version can be installed:
# install.packages("remotes")
remotes::install_github("pbsassess/sdmTMB", dependencies = TRUE)
If you have problems installing INLA, try installing it directly first.
There are some extra utilities in the sdmTMBextra package.
Overview
Analyzing geostatistical data (coordinatereferenced observations from some underlying spatial process) is becoming increasingly common in ecology. sdmTMB implements geostatistical spatial and spatiotemporal GLMMs using TMB for model fitting and RINLA to set up SPDE (stochastic partial differential equation) matrices. One common application is for species distribution models (SDMs), hence the package name. The goal of sdmTMB is to provide a fast, flexible, and userfriendly interface—similar to the popular R package glmmTMB—but with a focus on spatial and spatiotemporal models with an SPDE approach. We extend the generalized linear mixed models (GLMMs) familiar to ecologists to include the following optional features:
 spatial random fields
 spatiotemporal random fields that may be independent by year or modelled with random walks or autoregressive processes
 smooth terms for covariates, using the familiar
s()
notation from mgcv  breakpoint (hockeystick) or logistic covariates
 timevarying covariates (coefficients modelled as random walks)
 spatially varying coefficient models (SVCs)
 interpolation or forecasting over missing or future time slices
 a wide range of families: all standard R families plus
tweedie()
,nbinom1()
,nbinom2()
,lognormal()
, andstudent()
, plus some truncated and censored families  delta/hurdle models including
delta_gamma()
,delta_lognormal()
, anddelta_truncated_nbinom2()
Estimation is performed in sdmTMB via maximum marginal likelihood with the objective function calculated in TMB and minimized in R via stats::nlminb()
with the random effects integrated over via the Laplace approximation. The sdmTMB package also allows for models to be passed to Stan via tmbstan, allowing for Bayesian model estimation.
See ?sdmTMB
and ?predict.sdmTMB
for the most complete examples. Also see the vignettes (‘Articles’) on the documentation site and the preprint and appendices linked to below.
Getting help
For questions about how to use sdmTMB or interpret the models, please post on the discussion board. If you email a question, we are likely to respond on the discussion board with an anonymized version of your question (and without data) if we think it could be helpful to others. Please let us know if you don’t want us to do that.
For bugs or feature requests, please post in the issue tracker.
Slides and recordings from a workshop on sdmTMB.
Citation
To cite sdmTMB in publications use:
citation("sdmTMB")
Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett. 2022. sdmTMB: an R package for fast, flexible, and userfriendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545
Related software
sdmTMB is heavily inspired by the VAST R package:
Thorson, J.T. 2019. Guidance for decisions using the Vector Autoregressive SpatioTemporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fisheries Research 210: 143–161. https://doi.org/10.1016/j.fishres.2018.10.013.
and the glmmTMB R package:
Brooks, M.E., Kristensen, K., van Benthem, K.J., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Maechler, M., and Bolker, B.M. 2017. glmmTMB balances speed and flexibility among packages for zeroinflated generalized linear mixed modeling. The R Journal 9(2): 378–400. https://doi.org/10.32614/rj2017066.
INLA and inlabru can fit many of the same models as sdmTMB (and many more) in an approximate Bayesian inference framework.
mgcv can fit similar SPDEbased Gaussian random field models with code included in Miller et al. (2019).
A table in the sdmTMB preprint describes functionality and timing comparisons between sdmTMB, VAST, INLA/inlabru, and mgcv and the discussion makes suggestions about when you might choose one package over another.
Basic use
An sdmTMB model requires a data frame that contains a response column, columns for any predictors, and columns for spatial coordinates. It usually makes sense to convert the spatial coordinates to an equidistant projection such as UTMs such that distance remains constant throughout the study region [e.g., using sf::st_transform()
]. Here, we illustrate a spatial model fit to Pacific cod (Gadus macrocephalus) trawl survey data from Queen Charlotte Sound, BC, Canada. Our model contains a main effect of depth as a penalized smoother, a spatial random field, and Tweedie observation error. Our data frame pcod
(built into the package) has a column year
for the year of the survey, density
for density of Pacific cod in a given survey tow, present
for whether density > 0
, depth
for depth in meters of that tow, and spatial coordinates X
and Y
, which are UTM coordinates in kilometres.
#> # A tibble: 3 × 6
#> year density present depth X Y
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2003 113. 1 201 446. 5793.
#> 2 2003 41.7 1 212 446. 5800.
#> 3 2003 0 0 220 449. 5802.
We start by creating a mesh object that contains matrices to apply the SPDE approach.
Here, cutoff
defines the minimum allowed distance between points in the units of X
and Y
(km). Alternatively, we could have created any mesh via the INLA package and supplied it to make_mesh()
. We can inspect our mesh object with the associated plotting method plot(mesh)
.
Fit a spatial model with a smoother for depth:
fit < sdmTMB(
density ~ s(depth),
data = pcod,
mesh = mesh,
family = tweedie(link = "log"),
spatial = "on"
)
Print the model fit:
fit
#> Spatial model fit by ML ['sdmTMB']
#> Formula: density ~ s(depth)
#> Mesh: mesh
#> Data: pcod
#> Family: tweedie(link = 'log')
#>
#> coef.est coef.se
#> (Intercept) 2.37 0.21
#> sdepth 6.17 25.17
#>
#> Smooth terms:
#> Std. Dev.
#> sds(depth) 13.93
#>
#> Dispersion parameter: 12.69
#> Tweedie p: 1.58
#> Matern range: 16.39
#> Spatial SD: 1.86
#> ML criterion at convergence: 6402.136
#>
#> See ?tidy.sdmTMB to extract these values as a data frame.
The output indicates our model was fit by maximum (marginal) likelihood (ML
). We also see the formula, mesh, fitted data, and family. Next we see any estimated main effects including the linear component of the smoother (sdepth
), the standard deviation on the smoother weights (sds(depth)
), the Tweedie dispersion and power parameters, the Matérn range distance (distance at which points are effectively independent), the marginal spatial field standard deviation, and the negative log likelihood at convergence.
We can extract parameters as a data frame:
tidy(fit, conf.int = TRUE)
#> # A tibble: 1 × 5
#> term estimate std.error conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 2.37 0.215 1.95 2.79
tidy(fit, effects = "ran_pars", conf.int = TRUE)
#> # A tibble: 4 × 5
#> term estimate std.error conf.low conf.high
#> <chr> <dbl> <lgl> <dbl> <dbl>
#> 1 range 16.4 NA 9.60 28.0
#> 2 phi 12.7 NA 11.9 13.5
#> 3 sigma_O 1.86 NA 1.48 2.34
#> 4 tweedie_p 1.58 NA 1.56 1.60
Run some basic sanity checks on our model:
sanity(fit)
#> ✔ Nonlinear minimizer suggests successful convergence
#> ✔ Hessian matrix is positive definite
#> ✔ No extreme or very small eigenvalues detected
#> ✔ No gradients with respect to fixed effects are >= 0.001
#> ✔ No fixedeffect standard errors are NA
#> ✔ No standard errors look unreasonably large
#> ✔ No sigma parameters are < 0.01
#> ✔ No sigma parameters are > 100
#> ✔ Range parameter doesn't look unreasonably large
Use the visreg package to plot the smoother effect in link space with randomized quantile partial residuals:
Or on the response scale:
Predict on new data:
p < predict(fit, newdata = qcs_grid)
head(p)
#> # A tibble: 3 × 7
#> X Y depth est est_non_rf est_rf omega_s
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 456 5636 347. 3.06 3.08 0.0172 0.0172
#> 2 458 5636 223. 2.03 1.99 0.0459 0.0459
#> 3 460 5636 204. 2.89 2.82 0.0747 0.0747
ggplot(p, aes(X, Y, fill = exp(est))) + geom_raster() +
scale_fill_viridis_c(trans = "sqrt")
We could switch to a presenceabsence model by changing the response column and family:
Or a hurdle/delta model by changing the family:
fit < sdmTMB(
density ~ s(depth),
data = pcod,
mesh = mesh,
family = delta_gamma(link1 = "logit", link2 = "log"),
)
We could instead fit a spatiotemporal model by specifying the time
column and a spatiotemporal structure:
fit_spatiotemporal < sdmTMB(
density ~ s(depth, k = 5),
data = pcod,
mesh = mesh,
time = "year",
family = tweedie(link = "log"),
spatial = "off",
spatiotemporal = "ar1"
)
If we wanted to create an areaweighted standardized population index, we could predict on a grid covering the entire survey (qcs_grid
) with grid cell area 4 (2 x 2 km) and pass the predictions to get_index()
:
grid_yrs < replicate_df(qcs_grid, "year", unique(pcod$year))
p_st < predict(fit_spatiotemporal, newdata = grid_yrs,
return_tmb_object = TRUE)
index < get_index(p_st, area = rep(4, nrow(grid_yrs)))
ggplot(index, aes(year, est)) +
geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "grey90") +
geom_line(lwd = 1, colour = "grey30") +
labs(x = "Year", y = "Biomass (kg)")
Or the center of gravity:
cog < get_cog(p_st, format = "wide")
ggplot(cog, aes(est_x, est_y, colour = year)) +
geom_pointrange(aes(xmin = lwr_x, xmax = upr_x)) +
geom_pointrange(aes(ymin = lwr_y, ymax = upr_y)) +
scale_colour_viridis_c()
For more on these basic features, see the vignettes Intro to modelling with sdmTMB and Index standardization with sdmTMB.
Advanced functionality
Timevarying coefficients
Timevarying intercept:
fit < sdmTMB(
density ~ 0 + s(depth, k = 5),
time_varying = ~ 1,
data = pcod, mesh = mesh,
time = "year",
family = tweedie(link = "log"),
silent = FALSE # see progress
)
Timevarying (random walk) effect of depth:
fit < sdmTMB(
density ~ 1,
time_varying = ~ 0 + depth_scaled + depth_scaled2,
data = pcod, mesh = mesh,
time = "year",
family = tweedie(link = "log"),
spatial = "off",
spatiotemporal = "ar1",
silent = FALSE
)
See the vignette Intro to modelling with sdmTMB for more details.
Spatially varying coefficients (SVC)
Spatially varying effect of time:
pcod$year_scaled < as.numeric(scale(pcod$year))
fit < sdmTMB(
density ~ s(depth, k = 5) + year_scaled,
spatial_varying = ~ year_scaled,
data = pcod, mesh = mesh,
time = "year",
family = tweedie(link = "log"),
spatiotemporal = "off"
)
See zeta_s
in the output, which represents the coefficient varying in space. You’ll want to ensure you set up your model such that it ballpark has a mean of 0 (e.g., by including it in formula
too).
grid_yrs < replicate_df(qcs_grid, "year", unique(pcod$year))
grid_yrs$year_scaled < (grid_yrs$year  mean(pcod$year)) / sd(pcod$year)
p < predict(fit, newdata = grid_yrs) %>%
subset(year == 2011) # any year
#> Warning: The installed version of sdmTMB is newer than the version that was used to fit
#> this model. It is possible new parameters have been added to the TMB model
#> since you fit this model and that prediction will fail. We recommend you fit
#> and predict from an sdmTMB model with the same version.
ggplot(p, aes(X, Y, fill = zeta_s_year_scaled)) + geom_raster() +
scale_fill_gradient2()
See the vignette on Fitting spatial trend models with sdmTMB for more details.
Random intercepts
We can use the same syntax (1  group
) as lme4 or glmmTMB to fit random intercepts:
Breakpoint and threshold effects
fit < sdmTMB(
present ~ 1 + breakpt(depth_scaled),
data = pcod, mesh = mesh,
family = binomial(link = "logit")
)
fit < sdmTMB(
present ~ 1 + logistic(depth_scaled),
data = pcod, mesh = mesh,
family = binomial(link = "logit")
)
See the vignette on Threshold modeling with sdmTMB for more details.
Simulating data
Simulating data from scratch
predictor_dat < expand.grid(
X = seq(0, 1, length.out = 100), Y = seq(0, 1, length.out = 100)
)
mesh < make_mesh(predictor_dat, xy_cols = c("X", "Y"), cutoff = 0.05)
sim_dat < sdmTMB_simulate(
formula = ~ 1,
data = predictor_dat,
mesh = mesh,
family = poisson(link = "log"),
range = 0.3,
sigma_O = 0.4,
seed = 1,
B = 1 # B0 = intercept
)
head(sim_dat)
#> # A tibble: 6 × 7
#> X Y omega_s mu eta observed `(Intercept)`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0.154 2.33 0.846 1 1
#> 2 0.0101 0 0.197 2.23 0.803 0 1
#> 3 0.0202 0 0.240 2.14 0.760 2 1
#> 4 0.0303 0 0.282 2.05 0.718 2 1
#> 5 0.0404 0 0.325 1.96 0.675 3 1
#> 6 0.0505 0 0.367 1.88 0.633 2 1
# sample 200 points for fitting:
set.seed(1)
sim_dat_obs < sim_dat[sample(seq_len(nrow(sim_dat)), 200), ]
ggplot(sim_dat, aes(X, Y)) +
geom_raster(aes(fill = exp(eta))) + # mean without observation error
geom_point(aes(size = observed), data = sim_dat_obs, pch = 21) +
scale_fill_viridis_c() +
scale_size_area() +
coord_cartesian(expand = FALSE)
Fit to the simulated data:
mesh < make_mesh(sim_dat_obs, xy_cols = c("X", "Y"), cutoff = 0.05)
fit < sdmTMB(
observed ~ 1,
data = sim_dat_obs,
mesh = mesh,
family = poisson()
)
See ?sdmTMB_simulate
for more details.
Simulating from an existing fit
s < simulate(fit, nsim = 500)
dim(s)
#> [1] 969 500
s[1:3,1:4]
#> [,1] [,2] [,3] [,4]
#> [1,] 0 59.40310 83.20888 0.00000
#> [2,] 0 34.56408 0.00000 19.99839
#> [3,] 0 0.00000 0.00000 0.00000
See the vignette on Residual checking with sdmTMB, ?simulate.sdmTMB
, and ?dharma_residuals
for more details.
Sampling from the joint precision matrix
We can take samples from the implied parameter distribution assuming an MVN covariance matrix on the internal parameterization:
samps < gather_sims(fit, nsim = 1000)
ggplot(samps, aes(.value)) + geom_histogram() +
facet_wrap(~.variable, scales = "free_x")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
See ?gather_sims
and ?get_index_sims
for more details.
Calculating uncertainty on spatial predictions
The fastest way to get pointwise prediction uncertainty is to use the MVN samples:
p < predict(fit, newdata = predictor_dat, nsim = 500)
predictor_dat$se < apply(p, 1, sd)
ggplot(predictor_dat, aes(X, Y, fill = se)) +
geom_raster() +
scale_fill_viridis_c(option = "A") +
coord_cartesian(expand = FALSE)
Cross validation
sdmTMB has builtin functionality for crossvalidation. If we were to set a future::plan()
, the folds would be fit in parallel:
mesh < make_mesh(pcod, c("X", "Y"), cutoff = 10)
## Set parallel processing if desired:
# library(future)
# plan(multisession)
m_cv < sdmTMB_cv(
density ~ s(depth, k = 5),
data = pcod, mesh = mesh,
family = tweedie(link = "log"), k_folds = 2
)
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
# Sum of log likelihoods of leftout data:
m_cv$sum_loglik
#> [1] 7122.779
# Expected log pointwise predictive density from leftout data:
# (average likelihood density)
m_cv$elpd
#> [1] 1.005114
See ?sdmTMB_cv
for more details.
Priors
Priors/penalties can be placed on most parameters. For example, here we place a PC (penalized complexity) prior on the Matérn random field parameters, a standard normal prior on the effect of depth, a Normal(0, 10^2) prior on the intercept, and a halfnormal prior on the Tweedie dispersion parameter (phi
):
mesh < make_mesh(pcod, c("X", "Y"), cutoff = 10)
fit < sdmTMB(
density ~ depth_scaled,
data = pcod, mesh = mesh,
family = tweedie(),
priors = sdmTMBpriors(
matern_s = pc_matern(range_gt = 10, sigma_lt = 5),
b = normal(c(0, 0), c(1, 10)),
phi = halfnormal(0, 15)
)
)
We can visualize the PC Matérn prior:
plot_pc_matern(range_gt = 10, sigma_lt = 5)
See ?sdmTMBpriors
for more details.
Bayesian MCMC sampling with Stan
The fitted model can be passed to the tmbstan package to sample from the posterior with Stan. See the Bayesian vignette.
Turning off random fields
We can turn off the random fields for model comparison:
fit_sdmTMB < sdmTMB(
present ~ poly(depth_scaled, 2),
data = pcod, mesh = mesh,
spatial = "off",
family = binomial()
)
fit_glm < glm(
present ~ poly(depth_scaled, 2),
data = pcod,
family = binomial()
)
tidy(fit_sdmTMB)
#> # A tibble: 3 × 3
#> term estimate std.error
#> <chr> <dbl> <dbl>
#> 1 (Intercept) 0.426 0.0573
#> 2 poly(depth_scaled, 2)1 31.7 3.03
#> 3 poly(depth_scaled, 2)2 66.9 4.09
broom::tidy(fit_glm)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.426 0.0573 7.44 1.03e13
#> 2 poly(depth_scaled, 2)1 31.7 3.03 10.5 1.20e25
#> 3 poly(depth_scaled, 2)2 66.9 4.09 16.4 3.50e60
Using a custom INLA mesh
Defining a mesh directly with INLA:
bnd < INLA::inla.nonconvex.hull(cbind(pcod$X, pcod$Y), convex = 0.1)
mesh_inla < INLA::inla.mesh.2d(
boundary = bnd,
max.edge = c(25, 50)
)
mesh < make_mesh(pcod, c("X", "Y"), mesh = mesh_inla)
plot(mesh)
Barrier meshes
A barrier mesh limits correlation across barriers (e.g., land or water). See the example in ?add_barrier_mesh
.