performance  R Documentation 
TODO
## S4 method for signature 'FLQuants'
performance(
x,
statistics,
refpts = FLPar(),
years = setNames(list(dimnames(x[[1]])$year), nm = dims(x[[1]])$maxyear),
probs = c(0.1, 0.25, 0.5, 0.75, 0.9),
mp = NULL
)
## S4 method for signature 'FLStock'
performance(
x,
statistics,
refpts = FLPar(),
years = as.character(seq(dims(x)$minyear, dims(x)$maxyear)),
metrics = FLCore::metrics(x),
probs = NULL,
mp = NULL
)
## S4 method for signature 'FLStocks'
performance(
x,
statistics,
refpts = FLPar(),
years = dims(x[[1]])$maxyear,
metrics = FLCore::metrics,
probs = NULL,
grid = missing,
mp = NULL,
mc.cores = 1
)
## S4 method for signature 'list'
performance(
x,
statistics,
refpts = FLPar(),
years = dims(x[[1]])$maxyear,
probs = NULL,
grid = "missing",
mp = NULL,
mc.cores = 1,
...
)
## S4 method for signature 'FLom'
performance(x, refpts = x@refpts, metrics = NULL, statistics = NULL, ...)
statistics 
statistics to be computed, as formula, name and description, 
refpts 
Reference points for calculations, 
years 
Years on which statistics should be computed, defaults to last year of input FLQuants 
run 
Object holding the results of forward projections, as a named

Each statistics is an object of class list object, with three elements, the first two of them compulsory:
An unnamed element of class formula, e.g. yearMeans(SB/SB0)
.
name: A short name to be output on tables and plots, of class character, e.g. "SB/SB0".
desc: A longer description of the statistics, of class character, e.g. "Mean spawner biomass relative to unfished"
Each statistic formula
is evaluated against the metrics and refpts used
in the function call. Formulas can thus use (i) the names of the FLQuants
object or of the object returned by the call to metrics()
, (ii) of the
params in the refpts object and, for all classes but FLQuants
, (iii)
functions that can be called on object. See examples below for the
necessary matching between metrics, refpts and the statistics formulas.
data.table Results of computing performance statistics.
Iago Mosqueira, EC JRC
FLQuants
# LOAD example FLmse object
data(sol274)
# GENERATE pseudorun from last 20 years of OM
run < window(stock(om), start=2012, end=2021)
# DEFINE statistics
statistics < list(
dCatch=list(~yearMeans(C[, 1]/C[, dims(C)$year]),
name="mean(C[t] / C[t1])",
desc="Mean absolute proportional change in catch"),
varCatch=list(~yearVars(C),
name="var(C)",
desc="Variance in catch"),
varF=list(~yearVars(F),
name="var(F)",
desc="Variance in fishing mortality"))
# COMPUTE performance
performance(run, statistics, refpts=FLPar(MSY=110000),
metrics=list(C=catch, F=fbar), years=list(short=2016:2018, long=2016:2021))
# Minimum statistic, named list with formula and name
performance(run, statistics=list(CMSY=list(~yearMeans(C/MSY), name="CMSY")),
refpts=FLPar(MSY=110000), metrics=list(C=catch, F=fbar),
years=list(2012:2021))
# return quantiles
performance(run, statistics, refpts=FLPar(MSY=110000),
metrics=list(C=catch, F=fbar), years=list(2012:2021),
probs = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95))
# DEFINE statistics without summaries
statistics < list(
CMSY=list(~yearMeans(C/MSY),
name="CMSY",
desc="Catch over MSY"))
# COMPUTE performance
perf < performance(run, statistics, refpts=FLPar(MSY=110000),
metrics=list(C=catch), years=list(2012:2021))
# COMPUTE summaries
perf[, .(CMSY=mean(data))]
perf < performance(FLStocks(B=run, A=run), statistics,
refpts=FLPar(MSY=110000), metrics=list(C=catch), years=list(2012:2015))
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