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"),
  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()), or "ran_vcov" (list of variance covariance matrices for the random effects, by model and group).
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).

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 × 8
#>   model group_name term        level_ids estimate std.error conf.low conf.high
#>   <int> <chr>      <chr>       <chr>        <dbl>     <dbl>    <dbl>     <dbl>
#> 1     1 fyear      (Intercept) 2011        0.0163     0.187   -0.351    0.384 
#> 2     1 fyear      (Intercept) 2013        0.177      0.188   -0.192    0.545 
#> 3     1 fyear      (Intercept) 2015        0.232      0.189   -0.139    0.603 
#> 4     1 fyear      (Intercept) 2017       -0.431      0.205   -0.833   -0.0287