Turn sdmTMB model output into a tidy data frame

Usage

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

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), or "ran_vals" (individual random intercepts, if included; behaves like ranef()).
model: Which model to tidy if a delta model (1 or 2).
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).

Value

A data frame

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.

Examples

fit <- sdmTMB(density ~ poly(depth_scaled, 2, raw = TRUE),
  data = pcod_2011, mesh = pcod_mesh_2011,
  family = tweedie()
)
tidy(fit)
#> # A tibble: 3 × 5
#>   term                               estimate std.error conf.low conf.high
#>   <chr>                                 <dbl>     <dbl>    <dbl>     <dbl>
#> 1 (Intercept)                            3.65     0.281     3.10     4.20 
#> 2 poly(depth_scaled, 2, raw = TRUE)1    -1.54     0.186    -1.90    -1.17 
#> 3 poly(depth_scaled, 2, raw = TRUE)2    -1.11     0.101    -1.31    -0.913
tidy(fit, conf.int = TRUE)
#> # A tibble: 3 × 5
#>   term                               estimate std.error conf.low conf.high
#>   <chr>                                 <dbl>     <dbl>    <dbl>     <dbl>
#> 1 (Intercept)                            3.65     0.281     3.10     4.20 
#> 2 poly(depth_scaled, 2, raw = TRUE)1    -1.54     0.186    -1.90    -1.17 
#> 3 poly(depth_scaled, 2, raw = TRUE)2    -1.11     0.101    -1.31    -0.913
tidy(fit, "ran_pars", conf.int = TRUE)
#> # A tibble: 4 × 5
#>   term      estimate std.error conf.low conf.high
#>   <chr>        <dbl>     <dbl>    <dbl>     <dbl>
#> 1 range        19.1    14.0       4.58      80.0 
#> 2 phi          14.0     0.677    12.8       15.4 
#> 3 sigma_O       2.14    0.941     0.906      5.07
#> 4 tweedie_p     1.58    0.0153    1.55       1.61

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")
#> # A tibble: 4 × 5
#>   term       estimate std.error conf.low conf.high
#>   <chr>         <dbl>     <dbl>    <dbl>     <dbl>
#> 1 fyear_2011   0.0163     0.187   -0.351    0.384 
#> 2 fyear_2013   0.177      0.188   -0.192    0.545 
#> 3 fyear_2015   0.232      0.189   -0.139    0.603 
#> 4 fyear_2017  -0.431      0.205   -0.833   -0.0287