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Turn sdmTMB model output into a tidy data frame

Usage

# S3 method for sdmTMB
tidy(
  x,
  effects = c("fixed", "ran_pars", "ran_vals"),
  model = 1,
  conf.int = FALSE,
  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 × 3
#>   term                               estimate std.error
#>   <chr>                                 <dbl>     <dbl>
#> 1 (Intercept)                            3.65     0.281
#> 2 poly(depth_scaled, 2, raw = TRUE)1    -1.54     0.186
#> 3 poly(depth_scaled, 2, raw = TRUE)2    -1.11     0.101
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)
#> Standard errors intentionally omitted because they have been calculated in log
#> space.
#> # A tibble: 4 × 5
#>   term      estimate std.error conf.low conf.high
#>   <chr>        <dbl> <lgl>        <dbl>     <dbl>
#> 1 range        19.1  NA           4.58      80.0 
#> 2 phi          14.0  NA          12.8       15.4 
#> 3 sigma_O       2.14 NA           0.906      5.07
#> 4 tweedie_p     1.58 NA           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 × 3
#>   term       estimate std.error
#>   <chr>         <dbl>     <dbl>
#> 1 fyear_2011   0.0163     0.187
#> 2 fyear_2013   0.177      0.188
#> 3 fyear_2015   0.232      0.189
#> 4 fyear_2017  -0.431      0.205