emmeans.sdmTMB: Estimated marginal means with the 'emmeans' package with...

In sdmTMB: Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Estimated marginal means with the emmeans package with sdmTMB

Description

Methods for using the emmeans package with sdmTMB. The emmeans package computes estimated marginal means for the fixed effects.

For delta/hurdle models, you can specify which component to analyze using the model argument: model = 1 for the binomial component (encounter probability) or model = 2 for the positive component (e.g., gamma for delta_gamma()). By default, model = 1.

References

https://aosmith.rbind.io/2019/03/25/getting-started-with-emmeans/

Examples

mesh <- make_mesh(pcod_2011, c("X", "Y"), cutoff = 20)
fit <- sdmTMB(
  present ~ as.factor(year),
  data = pcod_2011, mesh = mesh,
  family = binomial()
)
fit
emmeans::emmeans(fit, ~ year)
emmeans::emmeans(fit, pairwise ~ year)
emmeans::emmeans(fit, pairwise ~ year, type = "response")
emmeans::emmeans(fit, pairwise ~ year, adjust = "none")

e <- emmeans::emmeans(fit, ~ year)
plot(e)

e <- emmeans::emmeans(fit, pairwise ~ year)
confint(e)
summary(e, infer = TRUE)
as.data.frame(e)

# interaction of factor with continuous predictor:
fit2 <- sdmTMB(
  present ~ depth_scaled * as.factor(year),
  data = pcod_2011, mesh = mesh,
  family = binomial()
)
fit2
# slopes for each level:
emmeans::emtrends(fit2, ~ year, var = "depth_scaled")
# test difference in slopes:
emmeans::emtrends(fit2, pairwise ~ year, var = "depth_scaled")
emmeans::emmip(fit2, year ~ depth_scaled,
  at = list(depth_scaled = seq(-2.5, 2.5, length.out = 50)), CIs = TRUE)

# delta/hurdle models:
fit_delta <- sdmTMB(
  density ~ as.factor(year),
  data = pcod_2011, spatial = "off",
  family = delta_gamma()
)
# binomial component (encounter probability):
emmeans::emmeans(fit_delta, ~ year, model = 1)
# positive component (gamma):
emmeans::emmeans(fit_delta, ~ year, model = 2)

sdmTMB documentation built on Nov. 26, 2025, 9:06 a.m.