Look up what a CEATTLE reported quantity is
Source:R/0-quantity_dictionary.R
quantity_dictionary.Rdfit$quantities uses the model's own abbreviated names. This returns the
table mapping each one to what it means, the units it is in, how it is
shaped, whether it holds a standard error, and what the same quantity is
called in the NOAA standardized assessment output.
Arguments
- quantity
Report names as they appear in
names(fit$quantities), defaultNULLfor every documented quantity.- process
Character vector restricting the table to one or more parts of the model:
"population","recruitment","mortality","fishing","reference_points","catchability","selectivity","growth","composition","predation","likelihood","linkage"or"internal".
Value
A data frame with one row per quantity and columns quantity,
process, meaning, units, dims, se and standard_label.
Details
Units follow the model throughout: numbers-at-age are thousands of fish
and weight-at-age is kg, so their product is mt. A quantity whose
units read "mt or thousands of fish" follows its fleet's
Observation_units column.
se = TRUE means the TMB template ADREPORTs the quantity, so fit$sdrep
holds a standard error for it and as.data.frame.Rceattle() can fill se,
lwr and upr. se = FALSE means no standard error exists anywhere on the
fit for that quantity. Nothing has a standard error when the fit was produced
with fit_control(getsd = FALSE), which leaves sdrep NULL.
standard_label gives the name the quantity takes in the NOAA standardized
assessment output that stockplotr and asar consume, so a table can be
relabelled for those tools; NA marks a quantity that standard has no
equivalent for. Rceattle's own names are kept as the primary vocabulary
because several quantities (predation, multispecies) have no standard name.
Every per-recruit reference point (SPR0, SPRlimit, SPRtarget,
SPRFinit, NbyageSPR) is computed only under msmMode = 0 and is exactly
zero on a multispecies fit: M there holds predation mortality, which
scales with predator abundance, so spawning output per recruit is not a
property of the prey stock alone.
See also
parameter_dictionary() for the estimated parameters,
as.data.frame.Rceattle() for these quantities in tidy form, and
fit_mod() for the fitted object they come from.
Examples
# The whole table
head(quantity_dictionary())
#> quantity process
#> 1 biomass population
#> 2 log_biomass population
#> 3 ssb population
#> 4 log_ssb population
#> 5 exploitable_biomass population
#> 6 biomass_depletion population
#> meaning
#> 1 Total stock biomass, summed over sexes and ages.
#> 2 Total stock biomass on the log scale; its standard error is the CV of biomass, which is the form an ABC buffer wants.
#> 3 Female spawning-stock biomass at the spawning month.
#> 4 Female spawning-stock biomass on the log scale; its standard error is the CV of SSB.
#> 5 Biomass selected by the fisheries, summed over fishery fleets; exactly zero for a survey-only species or when proj_F_prop is zero.
#> 6 Total biomass relative to unfished biomass, biomass / B0.
#> units dims se standard_label
#> 1 mt [nspp, nyrs] TRUE biomass
#> 2 log mt [nspp, nyrs] TRUE <NA>
#> 3 mt [nspp, nyrs] TRUE spawning_biomass
#> 4 log mt [nspp, nyrs] TRUE <NA>
#> 5 mt [nspp, nyrs] TRUE <NA>
#> 6 proportion [nspp, nyrs] TRUE <NA>
# What is ssb_depletion, and what units is it in?
quantity_dictionary("ssb_depletion")
#> quantity process
#> 1 ssb_depletion population
#> meaning
#> 1 Female spawning biomass relative to unfished, ssb / SB0; the quantity a Tier 3 harvest control rule compares against B40%. NA for a species with input numbers-at-age in single-species mode unless DynamicHCR = TRUE, where it is those numbers relative to themselves (1 at spawn_month = 0).
#> units dims se standard_label
#> 1 proportion [nspp, nyrs] TRUE relative_spawning_biomass
# Everything that holds a standard error
dict <- quantity_dictionary()
dict[dict$se, c("quantity", "meaning")]
#> quantity
#> 1 biomass
#> 2 log_biomass
#> 3 ssb
#> 4 log_ssb
#> 5 exploitable_biomass
#> 6 biomass_depletion
#> 7 ssb_depletion
#> 11 R
#> 12 log_R
#> 17 R_sd
#> 53 log_index_hat
#> 58 log_sel_at_age
#> 89 beta_linkage
#> 93 beta_linkage_obs
#> 103 pop_scalar
#> meaning
#> 1 Total stock biomass, summed over sexes and ages.
#> 2 Total stock biomass on the log scale; its standard error is the CV of biomass, which is the form an ABC buffer wants.
#> 3 Female spawning-stock biomass at the spawning month.
#> 4 Female spawning-stock biomass on the log scale; its standard error is the CV of SSB.
#> 5 Biomass selected by the fisheries, summed over fishery fleets; exactly zero for a survey-only species or when proj_F_prop is zero.
#> 6 Total biomass relative to unfished biomass, biomass / B0.
#> 7 Female spawning biomass relative to unfished, ssb / SB0; the quantity a Tier 3 harvest control rule compares against B40%. NA for a species with input numbers-at-age in single-species mode unless DynamicHCR = TRUE, where it is those numbers relative to themselves (1 at spawn_month = 0).
#> 11 Recruitment: numbers entering at the youngest age bin. For a species with input numbers-at-age (estDynamics > 0), the input recruits, with no standard error.
#> 12 Recruitment on the log scale; its standard error is the CV of recruitment.
#> 17 Standard deviation of the recruitment deviations, sigma_R, on the natural scale.
#> 53 Predicted survey index on the log scale.
#> 58 Log selectivity at age, reported only under fit_control(selectivity_se = TRUE), for a delta-method interval on the curve. One entry per estimated age-based lead fleet, sex, age at or above the fleet's first selected bin, and hindcast year; log_sel_at_age_index says which. Use exp(value +/- 1.96 * sd).
#> 89 Fitted coefficients of the environmental linkage formulas, one per row of the linkage table.
#> 93 QAR1 effect size scaling the latent deviate into the linked parameter; length 0 without a state-space covariate.
#> 103 Multiplier on user-supplied numbers-at-age; estimated for estDynamics = 2 under predation, 1 otherwise.
# Every reference point
quantity_dictionary(process = "reference_points")
#> quantity process
#> 1 Flimit reference_points
#> 2 Ftarget reference_points
#> 3 B0 reference_points
#> 4 SB0 reference_points
#> 5 SBF reference_points
#> 6 DynamicB0 reference_points
#> 7 DynamicSB0 reference_points
#> 8 DynamicSBF reference_points
#> 9 SPR0 reference_points
#> 10 SPRlimit reference_points
#> 11 SPRtarget reference_points
#> 12 SPRFinit reference_points
#> 13 NbyageSPR reference_points
#> 14 NByage0 reference_points
#> 15 NByageF reference_points
#> meaning
#> 1 Limit fishing mortality, the FOFL proxy (F35% under Tier 3) used in projections; NA for a species with input numbers-at-age.
#> 2 Target fishing mortality, the maximum FABC proxy (F40% under Tier 3) used in projections; NA for a species with input numbers-at-age.
#> 3 Total biomass at F = 0, carrying the estimated stock-recruit relationship.
#> 4 Female spawning biomass at F = 0; the B100% the Tier 3 proxies are taken from.
#> 5 Female spawning biomass at F = Ftarget; NA for a species with input numbers-at-age.
#> 6 Total biomass under the realized recruitment history with F set to zero. A species with input numbers-at-age keeps them.
#> 7 Female spawning biomass under the realized recruitment history with F set to zero. A species with input numbers-at-age keeps them.
#> 8 Female spawning biomass under the realized recruitment history with F set to Ftarget; NA for a species with input numbers-at-age.
#> 9 Spawning biomass per recruit at F = 0. Zero under msmMode > 0.
#> 10 Spawning biomass per recruit at F = Flimit. Zero under msmMode > 0; NA for a species with input numbers-at-age.
#> 11 Spawning biomass per recruit at F = Ftarget. Zero under msmMode > 0; NA for a species with input numbers-at-age.
#> 12 Spawning biomass per recruit at F = Finit. Zero under msmMode > 0.
#> 13 Survivors per recruit behind the four SPR schedules; the first dimension is F = 0, Flimit, Ftarget, Finit. Zero under msmMode > 0.
#> 14 Numbers at age at mean recruitment and F = 0, the age structure behind B0 and SB0.
#> 15 Numbers at age at mean recruitment and F = Ftarget, the age structure behind SBF.
#> units dims se standard_label
#> 1 yr^-1 [nspp] FALSE <NA>
#> 2 yr^-1 [nspp] FALSE <NA>
#> 3 mt [nspp, nyrs] FALSE <NA>
#> 4 mt [nspp, nyrs] FALSE spawning_biomass_zero
#> 5 mt [nspp, nyrs] FALSE <NA>
#> 6 mt [nspp, nyrs] FALSE <NA>
#> 7 mt [nspp, nyrs] FALSE <NA>
#> 8 mt [nspp, nyrs] FALSE <NA>
#> 9 kg per recruit [nspp] FALSE <NA>
#> 10 kg per recruit [nspp] FALSE <NA>
#> 11 kg per recruit [nspp] FALSE <NA>
#> 12 kg per recruit [nspp] FALSE <NA>
#> 13 fish per recruit [4, nspp, nages] FALSE <NA>
#> 14 thousands of fish [nspp, nsex, nages, nyrs] FALSE <NA>
#> 15 thousands of fish [nspp, nsex, nages, nyrs] FALSE <NA>
# Search the meanings by keyword
dict[grep("depletion", dict$meaning, ignore.case = TRUE), ]
#> [1] quantity process meaning units dims
#> [6] se standard_label
#> <0 rows> (or 0-length row.names)