Return a ready-to-plot table of spawning biomass, recruitment or mortality,
without navigating the nested tidy output. These functions select the same
estimates, uncertainty and units used in tidy_tam() and the dashboard.
Usage
get_ssb(..., model_list = NULL, interval = 0.95)
get_recruitment(..., model_list = NULL, interval = 0.95)
get_fbar(..., model_list = NULL, interval = 0.95)
get_mbar(..., model_list = NULL, interval = 0.95)
get_f(..., model_list = NULL, interval = 0.95)
get_m(..., model_list = NULL, interval = 0.95)Arguments
- ...
One or more fitted
tam_fitmodels ortam_refreporting references, or one named list of these objects.- model_list
A named list of models, instead of
....- interval
Confidence level in
(0, 1), default0.95. Fitted summaries are rebuilt when their cached interval differs. Reference uncertainty is retained as supplied, because it cannot be recalculated from a reporting reference.
Value
A data frame retaining available keys, est, confidence limits,
se, se_scale, unit and is_proj. Available metadata, such as SSB
timing, is retained. Annual summaries have year; surfaces also have age.
Details
get_ssb() and get_recruitment() return annual estimates. get_fbar()
and get_mbar() return the model's abundance-weighted means over the ages
chosen by F_settings$mean_ages and M_settings$mean_ages, respectively.
get_f() and get_m() return year-by-age surfaces; F is total fishing
mortality, summed over fleets. They do not return process deviations.
A single fit returns its table without a model column. Multiple fits and
named-list inputs add model, using the supplied names. Missing reference
quantities are omitted rather than reconstructed. Explicit reference
comparison_scales follow tidy_tam()'s conversion rules; otherwise known
incompatible units must be converted before combining models.
These are lightweight selectors. They never refit models or calculate observation OSA residuals or joint-sample process diagnostics. Supplied objects are not modified.
Examples
if (FALSE) { # \dontrun{
get_ssb(fit)
get_recruitment(fit, interval = 0.8)
get_fbar(Base = fit, Alternative = other_fit)
get_f(model_list = list(Base = fit, Alternative = other_fit))
} # }