Predict from an sdmTMB model — predict.sdmTMB • sdmTMB

Make predictions from an sdmTMB model. Predictions can be made on the original data or on new data.

Usage

# S3 method for class 'sdmTMB'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  se_fit = FALSE,
  re_form = NULL,
  re_form_iid = NULL,
  allow_new_levels = NULL,
  nsim = 0,
  sims_var = "est",
  model = c(NA, 1, 2),
  offset = NULL,
  mcmc_samples = NULL,
  nonlocal_newdata = NULL,
  return_tmb_object = FALSE,
  return_tmb_report = FALSE,
  return_tmb_data = FALSE,
  ...
)

Arguments

object: A model fitted with sdmTMB().
newdata: A data frame to make predictions on. It should contain the same predictor columns as the fitted data and, for spatiotemporal models, a time column with the same name as in the fitted data.
type: Should predictions be returned in link space (default) or response space?
se_fit: Should standard errors on predictions be calculated? Warning: can be slow for large datasets or high-resolution projections when random fields are included. For faster uncertainty estimation, either use re_form = NA to exclude random fields or use the nsim argument to simulate from the joint precision matrix.
re_form: NULL to include all spatial/spatiotemporal random fields in predictions. ~0 or NA for population-level predictions (predictions excluding spatial/spatiotemporal random fields). Often used with se_fit = TRUE to visualize marginal effects. Does not affect get_index() calculations.
re_form_iid: NULL to include all IID random intercepts/slopes in the predictions. ~0 or NA for population-level predictions. No other options (e.g., some but not all random intercepts) are not yet implemented. Only affects predictions with newdata. This does affect get_index().
allow_new_levels: Logical or NULL. Similar to glmmTMB's allow.new.levels. Allows predictions for previously unobserved levels in random effect grouping variables. If NULL (default), new levels are allowed when re_form_iid = NA or re_form_iid = ~0 and a warning is issued otherwise. If TRUE, new levels are explicitly allowed. If FALSE, a warning is issued if new levels are found. New levels are always treated as population-level predictions for the IID random effects (i.e., random effect value = 0).
nsim: If > 0, simulate from the joint precision matrix with nsim draws. Returns a matrix with one row per prediction location and one column per draw. By default, each column represents one draw of the linear predictor in link space; use type = "response" for response-space draws. Simulating from the joint precision matrix accounts for uncertainty in both fixed and random effects. Use this to derive uncertainty on predictions (e.g., apply(x, 1, sd)) or propagate uncertainty to derived quantities. This is the fastest way to characterize spatial uncertainty with sdmTMB.
sims_var: Experimental: Which TMB reported variable from the model should be extracted from the joint precision matrix simulation draws? Defaults to link-space predictions. Options include: "omega_s", "zeta_s", "epsilon_st", and "est_rf" (as described below). Other options will be passed verbatim.
model: Which component to predict from delta/hurdle models when nsim > 0 or mcmc_samples is supplied. NA (default) returns the combined prediction from both components; 1 returns the binomial component only; 2 returns the positive component only. Predictions are on the link or response scale depending on type. For regular predictions (without simulation), both components are returned. See the delta-model vignette.
offset: A numeric vector of optional offset values. When predictions are made with newdata or with options that internally rebuild prediction data (e.g., type = "response", se_fit = TRUE, or nsim > 0), the default NULL uses an offset of 0. The simplest predict(object) call on the original data uses the offset from the fitted model.
mcmc_samples: See extract_mcmc() in the sdmTMBextra package for more details and the Bayesian vignette. If specified, the predict function will return a matrix of a similar form as if nsim > 0 but representing Bayesian posterior samples from the Stan model.
nonlocal_newdata: An optional data frame overriding the nonlocal_formula covariate field used for prediction (e.g., for a counterfactual/scenario surface), with the same requirements as nonlocal_data in sdmTMB(). newdata's x/y and time columns always determine where predictions are projected to; this argument only controls where the underlying diffused covariate values come from. Defaults to NULL: if a grid was supplied at fit time, the fitted field is reused as-is (so newdata need not contain the diffusion covariate columns); otherwise the field is rebuilt from newdata's own covariate columns, as before.
return_tmb_object: Logical. If TRUE, include the TMB object in a list-format output. Necessary for the get_index() or get_cog() functions.
return_tmb_report: Logical: return the output from the TMB report? For regular prediction, this is all the reported variables at the MLE parameter values. For nsim > 0 or when mcmc_samples is supplied, this is a list with one element per sample; each element contains the report output for that sample.
return_tmb_data: Logical: return formatted data for TMB? Used internally.
...: Unused.

Value

If return_tmb_object = FALSE (and nsim = 0 and mcmc_samples = NULL):

A data frame:

est: Estimate in link or response space, depending on type
est_non_rf: Estimate from everything except spatial/spatiotemporal random fields (fixed effects, random intercepts, time-varying effects, etc.)
est_rf: Estimate from all random fields combined
omega_s: Spatial random field (models consistent spatial patterns)
zeta_s: Spatially varying coefficient field (models how effects vary across space)
epsilon_st: Spatiotemporal random field (models spatial patterns that vary over time)
nl_*: Nonlocal transformed covariate values (one column per nonlocal term; available when nonlocal_formula terms were fitted)

Delta/hurdle models return component-specific columns with 1 and 2 suffixes for the binomial and positive components, respectively (e.g., est1, est2, omega_s1, omega_s2). With type = "response", est is the combined response-scale prediction.

If return_tmb_object = TRUE (and nsim = 0 and mcmc_samples = NULL):

A list:

data: The data frame described above
report: The TMB report on parameter values
obj: The TMB object returned from the prediction run
fit_obj: The original TMB model object

In this case, you likely only need the data element as an end user. The other elements are included for other functions.

If nsim > 0 or mcmc_samples is not NULL:

A matrix:

Columns represent samples
Rows represent predictions, with one row per row of newdata

Examples


d <- pcod_2011
mesh <- make_mesh(d, c("X", "Y"), cutoff = 30) # a coarse mesh for example speed
m <- sdmTMB(
 data = d, formula = density ~ 0 + as.factor(year) + depth_scaled + depth_scaled2,
 time = "year", mesh = mesh, family = tweedie(link = "log")
)

# Predictions at original data locations -------------------------------

predictions <- predict(m)
head(predictions)
#> # A tibble: 6 × 17
#>    year     X     Y depth density present   lat   lon depth_mean depth_sd
#>   <int> <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl> <dbl>      <dbl>    <dbl>
#> 1  2011  435. 5718.   241    245.       1  51.6 -130.       5.16    0.445
#> 2  2011  487. 5719.    52      0        0  51.6 -129.       5.16    0.445
#> 3  2011  490. 5717.    47      0        0  51.6 -129.       5.16    0.445
#> 4  2011  545. 5717.   157      0        0  51.6 -128.       5.16    0.445
#> 5  2011  404. 5720.   398      0        0  51.6 -130.       5.16    0.445
#> 6  2011  420. 5721.   486      0        0  51.6 -130.       5.16    0.445
#> # ℹ 7 more variables: depth_scaled <dbl>, depth_scaled2 <dbl>, est <dbl>,
#> #   est_non_rf <dbl>, est_rf <dbl>, omega_s <dbl>, epsilon_st <dbl>

predictions$resids <- residuals(m) # randomized quantile residuals

library(ggplot2)
ggplot(predictions, aes(X, Y, col = resids)) + scale_colour_gradient2() +
  geom_point() + facet_wrap(~year)

hist(predictions$resids)

qqnorm(predictions$resids); abline(a = 0, b = 1)


# Predictions on new data ----------------------------------------------

qcs_grid_2011 <- replicate_df(qcs_grid, "year", unique(pcod_2011$year))
predictions <- predict(m, newdata = qcs_grid_2011)

# \donttest{
# A short function for plotting predictions:
plot_map <- function(dat, column = est) {
  ggplot(dat, aes(X, Y, fill = {{ column }})) +
    geom_raster() +
    facet_wrap(~year) +
    coord_fixed()
}

plot_map(predictions, exp(est)) +
  scale_fill_viridis_c(trans = "sqrt") +
  ggtitle("Prediction (fixed effects + all random effects)")


plot_map(predictions, exp(est_non_rf)) +
  ggtitle("Prediction (fixed effects and any time-varying effects)") +
  scale_fill_viridis_c(trans = "sqrt")


plot_map(predictions, est_rf) +
  ggtitle("All random field estimates") +
  scale_fill_gradient2()


plot_map(predictions, omega_s) +
  ggtitle("Spatial random effects only") +
  scale_fill_gradient2()


plot_map(predictions, epsilon_st) +
  ggtitle("Spatiotemporal random effects only") +
  scale_fill_gradient2()


# Visualizing a marginal effect ----------------------------------------

# See the visreg package or the ggeffects::ggeffect() or
# ggeffects::ggpredict() functions
# To do this manually:

nd <- data.frame(depth_scaled =
  seq(min(d$depth_scaled), max(d$depth_scaled), length.out = 100))
nd$depth_scaled2 <- nd$depth_scaled^2

# Because this is a spatiotemporal model, you'll need at least one time
# value. For these population-level predictions, if time isn't also a fixed
# effect, it doesn't matter what you pick:
nd$year <- 2011L # L: integer to match original data
p <- predict(m, newdata = nd, se_fit = TRUE, re_form = NA)
ggplot(p, aes(depth_scaled, exp(est),
  ymin = exp(est - 1.96 * est_se), ymax = exp(est + 1.96 * est_se))) +
  geom_line() + geom_ribbon(alpha = 0.4)


# Plotting marginal effect of a spline ---------------------------------

m_gam <- sdmTMB(
 data = d, formula = density ~ 0 + as.factor(year) + s(depth_scaled, k = 5),
 time = "year", mesh = mesh, family = tweedie(link = "log")
)
if (require("visreg", quietly = TRUE)) {
  visreg::visreg(m_gam, "depth_scaled")
}


# or manually:
nd <- data.frame(depth_scaled =
  seq(min(d$depth_scaled), max(d$depth_scaled), length.out = 100))
nd$year <- 2011L
p <- predict(m_gam, newdata = nd, se_fit = TRUE, re_form = NA)
ggplot(p, aes(depth_scaled, exp(est),
  ymin = exp(est - 1.96 * est_se), ymax = exp(est + 1.96 * est_se))) +
  geom_line() + geom_ribbon(alpha = 0.4)


# Forecasting ----------------------------------------------------------
mesh <- make_mesh(d, c("X", "Y"), cutoff = 15)

unique(d$year)
#> [1] 2011 2013 2015 2017
m <- sdmTMB(
  data = d, formula = density ~ 1,
  spatiotemporal = "AR1", # using AR(1) to have something to forecast with
  extra_time = 2019L, # `L` for integer to match our data
  spatial = "off",
  time = "year", mesh = mesh, family = tweedie(link = "log")
)

# Add a year to our grid:
grid2019 <- qcs_grid_2011[qcs_grid_2011$year == max(qcs_grid_2011$year), ]
grid2019$year <- 2019L # `L` because `year` is an integer in the data
qcsgrid_forecast <- rbind(qcs_grid_2011, grid2019)

predictions <- predict(m, newdata = qcsgrid_forecast)
plot_map(predictions, exp(est)) +
  scale_fill_viridis_c(trans = "log10")

plot_map(predictions, epsilon_st) +
  scale_fill_gradient2()


# Estimating local trends ----------------------------------------------

d <- pcod
d$year_scaled <- as.numeric(scale(d$year))
mesh <- make_mesh(pcod, c("X", "Y"), cutoff = 25)
m <- sdmTMB(data = d, formula = density ~ depth_scaled + depth_scaled2,
  mesh = mesh, family = tweedie(link = "log"),
  spatial_varying = ~ 0 + year_scaled, time = "year", spatiotemporal = "off")
nd <- replicate_df(qcs_grid, "year", unique(pcod$year))
nd$year_scaled <- (nd$year - mean(d$year)) / sd(d$year)
p <- predict(m, newdata = nd)

plot_map(subset(p, year == 2003), zeta_s_year_scaled) + # pick any year
  ggtitle("Spatial slopes") +
  scale_fill_gradient2()


plot_map(p, est_rf) +
  ggtitle("Random field estimates") +
  scale_fill_gradient2()


plot_map(p, exp(est_non_rf)) +
  ggtitle("Prediction (fixed effects only)") +
  scale_fill_viridis_c(trans = "sqrt")


plot_map(p, exp(est)) +
  ggtitle("Prediction (fixed effects + all random effects)") +
  scale_fill_viridis_c(trans = "sqrt")

# }