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Compute yardstick metrics for a fitted FIMS model

Source: R/augment-fims.R
get_fit_metrics.Rd

Extracts observed-vs-expected pairs from a FIMSFit object via augment.FIMSFit() and evaluates a yardstick::metric_set() over them.

Usage

get_fit_metrics(
  x,
  metrics = yardstick::metric_set(yardstick::rmse, yardstick::mae, yardstick::rsq),
  group_by = NULL,
  weighted = FALSE,
  ...
)

Arguments

x

A FIMSFit object returned from fit_fims().

metrics

A yardstick::metric_set(). Defaults to yardstick::metric_set(yardstick::rmse, yardstick::mae, yardstick::rsq). Any regression metric that accepts truth / estimate (and optionally case_weights) can be included.

group_by

A character vector of column names in the augmented tibble to group by before computing metrics. Common choices:

  • "label" – one row-set per data-stream label

  • "fleet" – one row-set per fleet

  • "distribution" – one row-set per likelihood distribution

  • c("label", "fleet") – per label × fleet combination Defaults to NULL (no grouping).

weighted

Logical (default FALSE). When TRUE, inverse-variance weights from uncertainty are passed to the metric functions via the case_weights argument. Only metrics that accept case_weights will use them; others silently ignore the column.

...

Additional arguments forwarded to augment.FIMSFit().

Value

A tibble::tibble() in the standard yardstick result format: columns .metric, .estimator, .estimate, plus any grouping columns.

Details

By default the metrics are computed over all data streams combined. Pass one or more column names to group_by to get per-stream breakdowns (e.g., group_by = "label" gives one row per data-stream label such as "landings_expected", "age_comp_expected", etc.).

See also

augment.FIMSFit(), yardstick::metric_set()

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)

# Overall RMSE / MAE / R² across all data streams
get_fit_metrics(fit)

# Per-data-stream: one row-set per label
# (e.g. "landings_expected", "index_expected", "age_comp_expected")
get_fit_metrics(fit, group_by = "label")

# Per-fleet with a custom metric set
get_fit_metrics(
  fit,
  metrics  = yardstick::metric_set(yardstick::rmse, yardstick::mape),
  group_by = "fleet"
)

# Inverse-variance weighted metrics
get_fit_metrics(fit, weighted = TRUE)
} # }