Tidy a FIMSFit object into a parameter-level tibble

Returns one row per estimated parameter following the generics::tidy() convention. Standard columns (term, estimate, std.error, statistic, p.value) are always present; FIMS-specific columns (module_name, module_id, estimation_type, gradient) are appended so the full context is available for filtering and plotting.

Usage

# S3 method for class 'FIMSFit'
tidy(
  x,
  parameters = c("fixed_effects", "random_effects"),
  conf.int = FALSE,
  conf.level = 0.95,
  ...
)

Arguments

x: A FIMSFit object returned from fit_fims().
parameters: Character vector controlling which estimation_type values to include. Defaults to c("fixed_effects", "random_effects"). Pass "derived_quantity" to include derived quantities such as spawning biomass and expected data values, or pass all three to get every row.
conf.int: Logical (default FALSE). When TRUE, conf.low and conf.high columns are added using a normal approximation: estimate ± qnorm((1 + conf.level) / 2) * std.error.
conf.level: Numeric (default 0.95). The confidence level used when conf.int = TRUE.
...: Unused; present for S3 method compatibility.

Value

A tibble::tibble() with columns:

term: Parameter label (from label in get_estimates()).
estimate: Point estimate at the MLE.
std.error: Standard error from TMB::sdreport().
statistic: Wald z-statistic (estimate / std.error).
p.value: Two-sided p-value for the z-test.
conf.low, conf.high: Confidence bounds (only when conf.int = TRUE).
module_name: Name of the FIMS module (e.g. "Selectivity").
module_id: Integer module identifier.
estimation_type: One of "fixed_effects", "random_effects", or "derived_quantity".
gradient: Gradient of the log-likelihood at the MLE. Values close to zero indicate a well-converged parameter.

Parameter types

FIMS distinguishes three estimation_type values:

"fixed_effects": Directly optimized parameters (selectivity, log_Fmort, log_q, …).
"random_effects": Integrated-out random effects (log_devs, …).
"derived_quantity": Model outputs that are not parameters (spawning biomass, expected catches, …). Uncertainty here comes from the delta method via TMB::sdreport().

Pass any subset of these strings to parameters to control which rows are returned.

Inference

statistic and p.value are computed as a two-sided Wald z-test: z = estimate / std.error, p = 2 * pnorm(-|z|). These are asymptotically valid for fixed effects under regularity conditions; treat them as approximate for random effects and derived quantities.

Examples

if (FALSE) { # \dontrun{
data("data_big")
data_4_model <- FIMSFrame(data_big)

fit <- create_default_parameters(
  configurations = create_default_configurations(data = data_4_model),
  data = data_4_model
) |>
  initialize_fims(data = data_4_model) |>
  fit_fims(optimize = TRUE)

# Fixed and random effects (default)
tidy(fit)

# Fixed effects only, with 95% confidence intervals
tidy(fit, parameters = "fixed_effects", conf.int = TRUE)

# All rows including derived quantities
tidy(fit, parameters = c("fixed_effects", "random_effects", "derived_quantity"))
} # }