tidy.sdmTMB: Turn sdmTMB model output into a tidy data frame

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

Turn sdmTMB model output into a tidy data frame

Description

Turn sdmTMB model output into a tidy data frame

Usage

## S3 method for class 'sdmTMB'
tidy(
  x,
  effects = c("fixed", "ran_pars", "ran_vals", "ran_vcov", "rsr"),
  model = 1,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  silent = FALSE,
  ...
)

## S3 method for class 'sdmTMB_cv'
tidy(x, ...)

Arguments

x	Output from sdmTMB().
effects	A character value. One of "fixed" ('fixed' or main-effect parameters), "ran_pars" (standard deviations, spatial range, and other random effect and dispersion-related terms), "ran_vals" (individual random intercepts or slopes, if included; behaves like ranef()), "ran_vcov" (list of variance covariance matrices for the random effects, by model and group), or "rsr" (Restricted Spatial Regression fixed-effect coefficients adjusted for spatial confounding with the random fields; Hanks et al. 2015; Diaz and Thorson 2025). To access RSR coefficients the model must be fitted with control = sdmTMBcontrol(get_rsr = TRUE).
model	Which model to tidy if a delta model (1 or 2). The model will be ignored when effects is "ran_vals" (all returned in a single dataframe)
conf.int	Include a confidence interval?
conf.level	Confidence level for CI.
exponentiate	Whether to exponentiate the fixed-effect coefficient estimates and confidence intervals.
silent	Omit any messages?
...	Extra arguments (not used).

Details

Follows the conventions of the broom and broom.mixed packages.

Currently, effects = "ran_pars" also includes dispersion-related terms (e.g., phi), which are not actually associated with random effects.

Standard errors for spatial variance terms fit in log space (e.g., variance terms, range, or parameters associated with the observation error) are omitted to avoid confusion. Confidence intervals are still available.

Value

A data frame

References

Restricted Spatial Regression (effects = "rsr"):

Diaz, R.R., and Thorson, J.T. 2025. When and How to Use Restricted Spatial Regression to Separate Environmental Effects from Spatial Confounding. EcoEvoRxiv. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32942/X28351")}.

Hanks, E.M., Schliep, E.M., Hooten, M.B., and Hoeting, J.A. 2015. Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification. Environmetrics 26(4): 243–254. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/env.2331")}.

Examples

fit <- sdmTMB(density ~ poly(depth_scaled, 2, raw = TRUE),
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie()
)
tidy(fit)
tidy(fit, conf.int = TRUE)
tidy(fit, "ran_pars", conf.int = TRUE)

pcod_2011$fyear <- as.factor(pcod_2011$year)
fit <- sdmTMB(density ~ poly(depth_scaled, 2, raw = TRUE) + (1 | fyear),
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie()
)
tidy(fit, "ran_vals")

sdmTMB documentation built on July 4, 2026, 1:06 a.m.