bioSum: Summary of the FLBEIA output
In flr/FLBEIA: Bio-Economic Impact Assessment of Management Strategies using FLR
View source: R/Results_Summary.R
bioSum R Documentation
Summary of the FLBEIA output
Description
Summarize the results of the simulation in data frames.
Usage
bioSum(
obj,
stknms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
long = FALSE,
scenario = "bc",
byyear = TRUE,
ssb_season = NULL,
brp = NULL
)
bioSumQ(obj, prob = c(0.95, 0.5, 0.05))
fltSum(
obj,
flnms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
byyear = TRUE,
long = FALSE,
InterestRate = 0.03,
scenario = "bc"
)
fltSumQ(obj, prob = c(0.95, 0.5, 0.05))
fltStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc",
verbose = TRUE
)
fltStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
mtStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
mtStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
mtSum(
obj,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
mtSumQ(obj, prob = c(0.95, 0.5, 0.05))
advSum(
obj,
stknms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
long = FALSE,
scenario = "bc"
)
advSumQ(obj, prob = c(0.95, 0.5, 0.05))
riskSum(
obj,
stknms = names(obj$biols),
Bpa,
Blim,
Prflim,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
scenario = "bc"
)
npv(
obj,
discF = 0.05,
y0,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
scenario = "bc"
)
npvQ(obj, prob = c(0.05, 0.5, 0.95))
vesselSum(
obj,
flnms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
vesselSumQ(obj, prob = c(0.95, 0.5, 0.05))
vesselStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
vesselStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
Arguments
obj
The output of the FLBEIA function.
stknms
Names of the stock for which the indicators will be calculated.
years
the names of the years for which the indicators will be calculated.
long
logical. The data frame should be constructed using long or wide format? Default FALSE.
scenario
a character string with the name of the scenario corresponding with obj. Default bc.
byyear
logical. The indicators should be provided at season or year level? Default TRUE.
ssb_season
If byyear = TRUE, the season in which ssb will be taken.
brp
a data frame with columns stock, iter and one colum per reference point with the value of the biological reference points per stock and iteration.
The used reference points are Bpa, Blim, Btarget, Fpa, Flim and Ftarget.
prob
a numeric vector with the probabilities used to calculate the quantiles.
flnms
Names of the fleet for which the indicators will be calculated.
InterestRate
Capital oportunity cost rate.
verbose
logical. If TRUE, prints the function steps.
Bpa
named numeric vector with one element per stock in stknms. The precautionary approach stock spawning biomass used in riskSum function to calculate biological risk yearly.
Blim
named numeric vector with one element per stock in stknms. The limit stock spawning biomass used in riskSum function to calculate biological risk yearly.
discF
Discount rate.
y0
character. Reference year.
ProfRef
named numeric vector with one element per fleet in flnms. The reference profit level used in riskSum function to calculate economic risk yearly.
Details
advSum, advSumQ: Data frame with the indicators related with the management advice (TAC). The indicators are:
"catch", "discards", "discRat", "landings", "quotaUpt" and "tac".
bioSum, bioSumQ: Data frame with the biological indicators. The indicators are:
"biomass", "catch", "catch.iyv", "discards", "disc.iyv", "f", "landings", "land.iyv", "rec" and "ssb".
fltSum, fltSumQ: Data frame with the indicators at fleet level. The indicators are:
"capacity", "catch", "costs", "discards", "discRat", "effort",
"fcosts", "gva", "grossValue", "landings", "fep", "nVessels", "price", "grossSurplus",
"quotaUpt", "salaries", "vcosts" and "profitability".
fltStkSum, fltStkSumQ: Data frame with the indicators at fleet and stock level. The indicators are:
"landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".
npv: A data frame with the net present value per fleet over the selected range of years.
mtSum, mtSumQ: Data frame with the indicators at metier level. The indicators are:
"effshare", "effort", "grossValue" and "vcost".
mtStkSum, mtStkSumQ: Data frame with the indicators at fleet and metier level. The indicators are:
"catch", "discards", "discRat", "landings" and "price".
riskSum: A data frame with the risk indicators. The indicators are:
"pBlim", "pBpa" and "pPrflim".
vesselSum, vesselSumQ: Data frame with the indicators at vessel level. The indicators are:
"catch", "costs", "discards", "discRat", "effort",
"fcosts", "gva", "grossValue", "landings", "fep", "price", "grossSurplus",
"quotaUpt", "salaries", "vcosts" and "profitability".
vesselStkSum, vesselStkSumQ: Data frame with the indicators at vessel and stock level. The indicators are:
"landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".
summary_flbeia: An array with four dimensions: stock, year, iteration,
indicator. The indicators are: recruitment, ssb, f, biomass, catch, landings and discards.
ecoSum_damara: ecoSum built in the framework of Damara project.
The data frames
Value
A data frame with columns for scenario, year, stock, iter, indicator, value,...
The data frames can be of wide or long format. In long format all the indicators are in the same column.
There is one column, indicator, for the name of the indicator and a second one value for the numeric value of the indicator.
In the wide format each of the indicators correspond with one column in the data frame.
The long format it is recommendable to work with ggplot2 functions for example while the wide format
it is more efficient for memory allocation and speed of computations.
The quantile version of the summaries, fooQ, returns the quantiles of the indicators.
In the long format as many columns as elements in prob are created. The name of the columns are
the elements in prob preceded by a q. In the wide format for each of the indicators as many
columns as elements in prob are created. The names of the colums are the elements in prob preceded by
q_name_of_the_indicator.
Examples
## Not run:
library(FLBEIA)
# Apply the summary functions to the examples runs in FLBEIA help page.
# Test the different arguments in summary function.
data(res_flbeia)
#------------------------------------------------
# Example One: One stock, one fleet, one iter.
#------------------------------------------------
# Wide format (default)
oneRes_bio <- bioSum(oneRes)
oneRes$fleets[[1]] <- setUnitsNA(oneRes$fleets[[1]])
oneRes_flt <- fltSum(oneRes)
oneRes_fltStk <- fltStkSum(oneRes)
oneRes_mt <- mtSum(oneRes)
oneRes_mtStk <- mtStkSum(oneRes)
oneRes_adv <- advSum(oneRes)
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
head(oneRes_bioQ)
head(oneRes_fltQ)
head(oneRes_fltStkQ)
head(oneRes_mtQ)
head(oneRes_mtStkQ)
head(oneRes_advQ)
# Long format for a range of years
oneRes_bio <- bioSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_flt <- fltSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mt <- mtSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mtStk <- mtStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_adv <- advSum(oneRes, long = TRUE, years = ac(2016:2020))
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
oneRes_bio <- bioSum(oneRes, byyear = FALSE)
oneRes_flt <- fltSum(oneRes, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, byyear = FALSE)
oneRes_mt <- mtSum(oneRes, byyear = FALSE)
oneRes_mtStk <- mtStkSum(oneRes, byyear = FALSE)
oneRes_adv <- advSum(oneRes) # Advice summary is only by year.
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
# Long format and seasonal
oneRes_bio <- bioSum(oneRes, long = TRUE)
oneRes_flt <- fltSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mt <- mtSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mtStk <- mtStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_adv <- advSum(oneRes, long = TRUE) # Advice summary is only by year.
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
#------------------------------------------------
# Example OneIt: As one but with iterations.
#------------------------------------------------
# Wide format (default)
oneItRes_bio <- bioSum(oneItRes, scenario = 'with_iters')
oneItRes$fleets[[1]] <- setUnitsNA(oneItRes$fleets[[1]])
oneItRes_flt <- fltSum(oneItRes, scenario = 'with_iters')
oneItRes_fltStk <- fltStkSum(oneItRes, scenario = 'with_iters')
oneItRes_mt <- mtSum(oneItRes, scenario = 'with_iters')
oneItRes_mtStk <- mtStkSum(oneItRes, scenario = 'with_iters')
oneItRes_adv <- advSum(oneItRes, scenario = 'with_iters')
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Long format for a range of years
oneItRes_bio <- bioSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_flt <- fltSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mt <- mtSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mtStk <- mtStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_adv <- advSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
oneItRes_bio <- bioSum(oneItRes, byyear = FALSE)
oneItRes_flt <- fltSum(oneItRes, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, byyear = FALSE)
oneItRes_mt <- mtSum(oneItRes, byyear = FALSE)
oneItRes_mtStk <- mtStkSum(oneItRes, byyear = FALSE)
oneItRes_adv <- advSum(oneItRes) # Advice summary is only by year.
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Long format and seasonal
oneItRes_bio <- bioSum(oneItRes, long = TRUE) # Biol summary is only by year.
oneItRes_flt <- fltSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mt <- mtSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mtStk <- mtStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_adv <- advSum(oneItRes, long = TRUE) # Advice summary is only by year.
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
oneItRes_risk <- riskSum( oneItRes, Bpa = c(stk1= 900), Blim = c(stk1 = 600),
Prflim = c(fl1 = 0), scenario = 'alternative')
oneItRes_npv <- npv(oneItRes, y0 = '2014')
#------------------------------------------------
# Example Multi: Two stock, two fleet, four iters.
#------------------------------------------------
# Wide format (default)
multiRes_bio <- bioSum(multiRes)
multiRes$fleets <- FLFleetsExt(lapply(multiRes$fleets, function(x) setUnitsNA(x)))
multiRes_flt <- fltSum(multiRes)
multiRes_fltStk <- fltStkSum(multiRes)
multiRes_mt <- mtSum(multiRes)
multiRes_mtStk <- mtStkSum(multiRes)
multiRes_adv <- advSum(multiRes)
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Long format for a range of years
multiRes_bio <- bioSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_flt <- fltSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mt <- mtSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mtStk <- mtStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_adv <- advSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
multiRes_bio <- bioSum(multiRes, byyear = FALSE)
multiRes_flt <- fltSum(multiRes, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, byyear = FALSE)
multiRes_mt <- mtSum(multiRes, byyear = FALSE)
multiRes_mtStk <- mtStkSum(multiRes, byyear = FALSE)
multiRes_adv <- advSum(multiRes) # Advice summary is only by year.
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Long format and seasonal
multiRes_bio <- bioSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_flt <- fltSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mt <- mtSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mtStk <- mtStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_adv <- advSum(multiRes, long = TRUE) # Advice summary is only by year.
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
multiRes_npv <- npv(multiRes, y0 = '2014')
risk_multiRes <- riskSum( multiRes, Bpa = c(stk1= 135000, stk2 = 124000),
Blim = c(stk1= 96000, stk2 = 89000), Prflim = c(fl1 = 0, fl2 = 0),
scenario = 'alternative')
## End(Not run)
flr/FLBEIA documentation built on Feb. 22, 2024, 1:36 a.m.
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View source: R/Results_Summary.R
| bioSum | R Documentation |
Summary of the FLBEIA output
Description
Summarize the results of the simulation in data frames.
Usage
bioSum(
obj,
stknms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
long = FALSE,
scenario = "bc",
byyear = TRUE,
ssb_season = NULL,
brp = NULL
)
bioSumQ(obj, prob = c(0.95, 0.5, 0.05))
fltSum(
obj,
flnms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
byyear = TRUE,
long = FALSE,
InterestRate = 0.03,
scenario = "bc"
)
fltSumQ(obj, prob = c(0.95, 0.5, 0.05))
fltStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc",
verbose = TRUE
)
fltStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
mtStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
mtStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
mtSum(
obj,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
mtSumQ(obj, prob = c(0.95, 0.5, 0.05))
advSum(
obj,
stknms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
long = FALSE,
scenario = "bc"
)
advSumQ(obj, prob = c(0.95, 0.5, 0.05))
riskSum(
obj,
stknms = names(obj$biols),
Bpa,
Blim,
Prflim,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
scenario = "bc"
)
npv(
obj,
discF = 0.05,
y0,
flnms = names(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
scenario = "bc"
)
npvQ(obj, prob = c(0.05, 0.5, 0.95))
vesselSum(
obj,
flnms = "all",
years = dimnames(obj$biols[[1]]@n)$year,
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
vesselSumQ(obj, prob = c(0.95, 0.5, 0.05))
vesselStkSum(
obj,
flnms = names(obj$fleets),
stknms = catchNames(obj$fleets),
years = dimnames(obj$biols[[1]]@n)[[2]],
byyear = TRUE,
long = FALSE,
scenario = "bc"
)
vesselStkSumQ(obj, prob = c(0.95, 0.5, 0.05))
Arguments
obj |
The output of the FLBEIA function. |
stknms |
Names of the stock for which the indicators will be calculated. |
years |
the names of the years for which the indicators will be calculated. |
long |
logical. The data frame should be constructed using long or wide format? Default FALSE. |
scenario |
a character string with the name of the scenario corresponding with obj. Default bc. |
byyear |
logical. The indicators should be provided at season or year level? Default TRUE. |
ssb_season |
If byyear = TRUE, the season in which ssb will be taken. |
brp |
a data frame with columns stock, iter and one colum per reference point with the value of the biological reference points per stock and iteration. The used reference points are Bpa, Blim, Btarget, Fpa, Flim and Ftarget. |
prob |
a numeric vector with the probabilities used to calculate the quantiles. |
flnms |
Names of the fleet for which the indicators will be calculated. |
InterestRate |
Capital oportunity cost rate. |
verbose |
logical. If TRUE, prints the function steps. |
Bpa |
named numeric vector with one element per stock in stknms. The precautionary approach stock spawning biomass used in riskSum function to calculate biological risk yearly. |
Blim |
named numeric vector with one element per stock in stknms. The limit stock spawning biomass used in riskSum function to calculate biological risk yearly. |
discF |
Discount rate. |
y0 |
character. Reference year. |
ProfRef |
named numeric vector with one element per fleet in flnms. The reference profit level used in riskSum function to calculate economic risk yearly. |
Details
advSum, advSumQ: Data frame with the indicators related with the management advice (TAC). The indicators are: "catch", "discards", "discRat", "landings", "quotaUpt" and "tac".
bioSum, bioSumQ: Data frame with the biological indicators. The indicators are: "biomass", "catch", "catch.iyv", "discards", "disc.iyv", "f", "landings", "land.iyv", "rec" and "ssb".
fltSum, fltSumQ: Data frame with the indicators at fleet level. The indicators are: "capacity", "catch", "costs", "discards", "discRat", "effort", "fcosts", "gva", "grossValue", "landings", "fep", "nVessels", "price", "grossSurplus", "quotaUpt", "salaries", "vcosts" and "profitability".
fltStkSum, fltStkSumQ: Data frame with the indicators at fleet and stock level. The indicators are: "landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".
npv: A data frame with the net present value per fleet over the selected range of years.
mtSum, mtSumQ: Data frame with the indicators at metier level. The indicators are: "effshare", "effort", "grossValue" and "vcost".
mtStkSum, mtStkSumQ: Data frame with the indicators at fleet and metier level. The indicators are: "catch", "discards", "discRat", "landings" and "price".
riskSum: A data frame with the risk indicators. The indicators are: "pBlim", "pBpa" and "pPrflim".
vesselSum, vesselSumQ: Data frame with the indicators at vessel level. The indicators are: "catch", "costs", "discards", "discRat", "effort", "fcosts", "gva", "grossValue", "landings", "fep", "price", "grossSurplus", "quotaUpt", "salaries", "vcosts" and "profitability".
vesselStkSum, vesselStkSumQ: Data frame with the indicators at vessel and stock level. The indicators are: "landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".
summary_flbeia: An array with four dimensions: stock, year, iteration, indicator. The indicators are: recruitment, ssb, f, biomass, catch, landings and discards.
ecoSum_damara: ecoSum built in the framework of Damara project.
The data frames
Value
A data frame with columns for scenario, year, stock, iter, indicator, value,...
The data frames can be of wide or long format. In long format all the indicators are in the same column. There is one column, indicator, for the name of the indicator and a second one value for the numeric value of the indicator. In the wide format each of the indicators correspond with one column in the data frame. The long format it is recommendable to work with ggplot2 functions for example while the wide format it is more efficient for memory allocation and speed of computations.
The quantile version of the summaries, fooQ, returns the quantiles of the indicators. In the long format as many columns as elements in prob are created. The name of the columns are the elements in prob preceded by a q. In the wide format for each of the indicators as many columns as elements in prob are created. The names of the colums are the elements in prob preceded by q_name_of_the_indicator.
Examples
## Not run:
library(FLBEIA)
# Apply the summary functions to the examples runs in FLBEIA help page.
# Test the different arguments in summary function.
data(res_flbeia)
#------------------------------------------------
# Example One: One stock, one fleet, one iter.
#------------------------------------------------
# Wide format (default)
oneRes_bio <- bioSum(oneRes)
oneRes$fleets[[1]] <- setUnitsNA(oneRes$fleets[[1]])
oneRes_flt <- fltSum(oneRes)
oneRes_fltStk <- fltStkSum(oneRes)
oneRes_mt <- mtSum(oneRes)
oneRes_mtStk <- mtStkSum(oneRes)
oneRes_adv <- advSum(oneRes)
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
head(oneRes_bioQ)
head(oneRes_fltQ)
head(oneRes_fltStkQ)
head(oneRes_mtQ)
head(oneRes_mtStkQ)
head(oneRes_advQ)
# Long format for a range of years
oneRes_bio <- bioSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_flt <- fltSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mt <- mtSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mtStk <- mtStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_adv <- advSum(oneRes, long = TRUE, years = ac(2016:2020))
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
oneRes_bio <- bioSum(oneRes, byyear = FALSE)
oneRes_flt <- fltSum(oneRes, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, byyear = FALSE)
oneRes_mt <- mtSum(oneRes, byyear = FALSE)
oneRes_mtStk <- mtStkSum(oneRes, byyear = FALSE)
oneRes_adv <- advSum(oneRes) # Advice summary is only by year.
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
# Long format and seasonal
oneRes_bio <- bioSum(oneRes, long = TRUE)
oneRes_flt <- fltSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mt <- mtSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mtStk <- mtStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_adv <- advSum(oneRes, long = TRUE) # Advice summary is only by year.
oneRes_bioQ <- bioSumQ(oneRes_bio)
oneRes_fltQ <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ <- mtSumQ(oneRes_mt)
oneRes_mtStkQ <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ <- advSumQ(oneRes_adv)
#------------------------------------------------
# Example OneIt: As one but with iterations.
#------------------------------------------------
# Wide format (default)
oneItRes_bio <- bioSum(oneItRes, scenario = 'with_iters')
oneItRes$fleets[[1]] <- setUnitsNA(oneItRes$fleets[[1]])
oneItRes_flt <- fltSum(oneItRes, scenario = 'with_iters')
oneItRes_fltStk <- fltStkSum(oneItRes, scenario = 'with_iters')
oneItRes_mt <- mtSum(oneItRes, scenario = 'with_iters')
oneItRes_mtStk <- mtStkSum(oneItRes, scenario = 'with_iters')
oneItRes_adv <- advSum(oneItRes, scenario = 'with_iters')
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Long format for a range of years
oneItRes_bio <- bioSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_flt <- fltSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mt <- mtSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mtStk <- mtStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_adv <- advSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
oneItRes_bio <- bioSum(oneItRes, byyear = FALSE)
oneItRes_flt <- fltSum(oneItRes, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, byyear = FALSE)
oneItRes_mt <- mtSum(oneItRes, byyear = FALSE)
oneItRes_mtStk <- mtStkSum(oneItRes, byyear = FALSE)
oneItRes_adv <- advSum(oneItRes) # Advice summary is only by year.
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
# Long format and seasonal
oneItRes_bio <- bioSum(oneItRes, long = TRUE) # Biol summary is only by year.
oneItRes_flt <- fltSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mt <- mtSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mtStk <- mtStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_adv <- advSum(oneItRes, long = TRUE) # Advice summary is only by year.
oneItRes_bioQ <- bioSumQ(oneItRes_bio)
oneItRes_fltQ <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ <- advSumQ(oneItRes_adv)
oneItRes_risk <- riskSum( oneItRes, Bpa = c(stk1= 900), Blim = c(stk1 = 600),
Prflim = c(fl1 = 0), scenario = 'alternative')
oneItRes_npv <- npv(oneItRes, y0 = '2014')
#------------------------------------------------
# Example Multi: Two stock, two fleet, four iters.
#------------------------------------------------
# Wide format (default)
multiRes_bio <- bioSum(multiRes)
multiRes$fleets <- FLFleetsExt(lapply(multiRes$fleets, function(x) setUnitsNA(x)))
multiRes_flt <- fltSum(multiRes)
multiRes_fltStk <- fltStkSum(multiRes)
multiRes_mt <- mtSum(multiRes)
multiRes_mtStk <- mtStkSum(multiRes)
multiRes_adv <- advSum(multiRes)
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Long format for a range of years
multiRes_bio <- bioSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_flt <- fltSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mt <- mtSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mtStk <- mtStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_adv <- advSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Wide format with seasonal disaggregation
# (Note: No seasonal disagregation available for adv summaries)
multiRes_bio <- bioSum(multiRes, byyear = FALSE)
multiRes_flt <- fltSum(multiRes, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, byyear = FALSE)
multiRes_mt <- mtSum(multiRes, byyear = FALSE)
multiRes_mtStk <- mtStkSum(multiRes, byyear = FALSE)
multiRes_adv <- advSum(multiRes) # Advice summary is only by year.
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
# Long format and seasonal
multiRes_bio <- bioSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_flt <- fltSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mt <- mtSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mtStk <- mtStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_adv <- advSum(multiRes, long = TRUE) # Advice summary is only by year.
multiRes_bioQ <- bioSumQ(multiRes_bio)
multiRes_fltQ <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ <- mtSumQ(multiRes_mt)
multiRes_mtStkQ <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ <- advSumQ(multiRes_adv)
multiRes_npv <- npv(multiRes, y0 = '2014')
risk_multiRes <- riskSum( multiRes, Bpa = c(stk1= 135000, stk2 = 124000),
Blim = c(stk1= 96000, stk2 = 89000), Prflim = c(fl1 = 0, fl2 = 0),
scenario = 'alternative')
## End(Not run)
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