TODO
# S4 method for 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 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 FLStocks
performance(
x,
statistics,
refpts = FLPar(),
years = dims(x[[1]])$maxyear,
metrics = FLCore::metrics,
probs = NULL,
grid = missing,
mp = NULL,
mc.cores = 1
)
# S4 method for list
performance(
x,
statistics,
refpts = FLPar(),
years = dims(x[[1]])$maxyear,
probs = NULL,
grid = "missing",
mp = NULL,
mc.cores = 1,
...
)
# S4 method for FLom
performance(x, refpts = x@refpts, ...)statistics to be computed, as formula, name and description, list
Reference points for calculations, list
Years on which statistics should be computed, defaults to last year of input FLQuants
Object holding the results of forward projections, as a named
FLQuants
data.table Results of computing performance statistics.
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.
FLQuants
# LOAD example FLmse object
data(sol274)
#> Warning: namespace ‘DBI’ is not available and has been replaced
#> by .GlobalEnv when processing object ‘om’
# GENERATE pseudo-run 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[t-1])",
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(2016:2021))
#> statistic year data iter name
#> 1: dCatch 2021 0.902979992 1 mean(C[t] / C[t-1])
#> 2: dCatch 2021 0.902979992 2 mean(C[t] / C[t-1])
#> 3: dCatch 2021 0.902979992 3 mean(C[t] / C[t-1])
#> 4: dCatch 2021 0.902979992 4 mean(C[t] / C[t-1])
#> 5: dCatch 2021 0.902979992 5 mean(C[t] / C[t-1])
#> ---
#> 296: varF 2021 0.022486666 96 var(F)
#> 297: varF 2021 0.007926315 97 var(F)
#> 298: varF 2021 0.011982839 98 var(F)
#> 299: varF 2021 0.012743466 99 var(F)
#> 300: varF 2021 0.015744298 100 var(F)
# 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))
#> statistic year data iter name
#> 1: CMSY 2021 0.1193508 1 CMSY
#> 2: CMSY 2021 0.1193508 2 CMSY
#> 3: CMSY 2021 0.1193508 3 CMSY
#> 4: CMSY 2021 0.1193508 4 CMSY
#> 5: CMSY 2021 0.1193508 5 CMSY
#> 6: CMSY 2021 0.1193508 6 CMSY
#> 7: CMSY 2021 0.1193508 7 CMSY
#> 8: CMSY 2021 0.1193508 8 CMSY
#> 9: CMSY 2021 0.1193508 9 CMSY
#> 10: CMSY 2021 0.1193508 10 CMSY
#> 11: CMSY 2021 0.1193508 11 CMSY
#> 12: CMSY 2021 0.1193508 12 CMSY
#> 13: CMSY 2021 0.1193508 13 CMSY
#> 14: CMSY 2021 0.1193508 14 CMSY
#> 15: CMSY 2021 0.1193508 15 CMSY
#> 16: CMSY 2021 0.1193508 16 CMSY
#> 17: CMSY 2021 0.1193508 17 CMSY
#> 18: CMSY 2021 0.1193508 18 CMSY
#> 19: CMSY 2021 0.1193508 19 CMSY
#> 20: CMSY 2021 0.1193508 20 CMSY
#> 21: CMSY 2021 0.1193508 21 CMSY
#> 22: CMSY 2021 0.1193508 22 CMSY
#> 23: CMSY 2021 0.1193508 23 CMSY
#> 24: CMSY 2021 0.1193508 24 CMSY
#> 25: CMSY 2021 0.1193508 25 CMSY
#> 26: CMSY 2021 0.1193508 26 CMSY
#> 27: CMSY 2021 0.1193508 27 CMSY
#> 28: CMSY 2021 0.1193508 28 CMSY
#> 29: CMSY 2021 0.1193508 29 CMSY
#> 30: CMSY 2021 0.1193508 30 CMSY
#> 31: CMSY 2021 0.1193508 31 CMSY
#> 32: CMSY 2021 0.1193508 32 CMSY
#> 33: CMSY 2021 0.1193508 33 CMSY
#> 34: CMSY 2021 0.1193508 34 CMSY
#> 35: CMSY 2021 0.1193508 35 CMSY
#> 36: CMSY 2021 0.1193508 36 CMSY
#> 37: CMSY 2021 0.1193508 37 CMSY
#> 38: CMSY 2021 0.1193508 38 CMSY
#> 39: CMSY 2021 0.1193508 39 CMSY
#> 40: CMSY 2021 0.1193508 40 CMSY
#> 41: CMSY 2021 0.1193508 41 CMSY
#> 42: CMSY 2021 0.1193508 42 CMSY
#> 43: CMSY 2021 0.1193508 43 CMSY
#> 44: CMSY 2021 0.1193508 44 CMSY
#> 45: CMSY 2021 0.1193508 45 CMSY
#> 46: CMSY 2021 0.1193508 46 CMSY
#> 47: CMSY 2021 0.1193508 47 CMSY
#> 48: CMSY 2021 0.1193508 48 CMSY
#> 49: CMSY 2021 0.1193508 49 CMSY
#> 50: CMSY 2021 0.1193508 50 CMSY
#> 51: CMSY 2021 0.1193508 51 CMSY
#> 52: CMSY 2021 0.1193508 52 CMSY
#> 53: CMSY 2021 0.1193508 53 CMSY
#> 54: CMSY 2021 0.1193508 54 CMSY
#> 55: CMSY 2021 0.1193508 55 CMSY
#> 56: CMSY 2021 0.1193508 56 CMSY
#> 57: CMSY 2021 0.1193508 57 CMSY
#> 58: CMSY 2021 0.1193508 58 CMSY
#> 59: CMSY 2021 0.1193508 59 CMSY
#> 60: CMSY 2021 0.1193508 60 CMSY
#> 61: CMSY 2021 0.1193508 61 CMSY
#> 62: CMSY 2021 0.1193508 62 CMSY
#> 63: CMSY 2021 0.1193508 63 CMSY
#> 64: CMSY 2021 0.1193508 64 CMSY
#> 65: CMSY 2021 0.1193508 65 CMSY
#> 66: CMSY 2021 0.1193508 66 CMSY
#> 67: CMSY 2021 0.1193508 67 CMSY
#> 68: CMSY 2021 0.1193508 68 CMSY
#> 69: CMSY 2021 0.1193508 69 CMSY
#> 70: CMSY 2021 0.1193508 70 CMSY
#> 71: CMSY 2021 0.1193508 71 CMSY
#> 72: CMSY 2021 0.1193508 72 CMSY
#> 73: CMSY 2021 0.1193508 73 CMSY
#> 74: CMSY 2021 0.1193508 74 CMSY
#> 75: CMSY 2021 0.1193508 75 CMSY
#> 76: CMSY 2021 0.1193508 76 CMSY
#> 77: CMSY 2021 0.1193508 77 CMSY
#> 78: CMSY 2021 0.1193508 78 CMSY
#> 79: CMSY 2021 0.1193508 79 CMSY
#> 80: CMSY 2021 0.1193508 80 CMSY
#> 81: CMSY 2021 0.1193508 81 CMSY
#> 82: CMSY 2021 0.1193508 82 CMSY
#> 83: CMSY 2021 0.1193508 83 CMSY
#> 84: CMSY 2021 0.1193508 84 CMSY
#> 85: CMSY 2021 0.1193508 85 CMSY
#> 86: CMSY 2021 0.1193508 86 CMSY
#> 87: CMSY 2021 0.1193508 87 CMSY
#> 88: CMSY 2021 0.1193508 88 CMSY
#> 89: CMSY 2021 0.1193508 89 CMSY
#> 90: CMSY 2021 0.1193508 90 CMSY
#> 91: CMSY 2021 0.1193508 91 CMSY
#> 92: CMSY 2021 0.1193508 92 CMSY
#> 93: CMSY 2021 0.1193508 93 CMSY
#> 94: CMSY 2021 0.1193508 94 CMSY
#> 95: CMSY 2021 0.1193508 95 CMSY
#> 96: CMSY 2021 0.1193508 96 CMSY
#> 97: CMSY 2021 0.1193508 97 CMSY
#> 98: CMSY 2021 0.1193508 98 CMSY
#> 99: CMSY 2021 0.1193508 99 CMSY
#> 100: CMSY 2021 0.1193508 100 CMSY
#> statistic year data iter name
# 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))
#> statistic year name data prob
#> 1: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.05
#> 2: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.10
#> 3: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.25
#> 4: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.50
#> 5: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.75
#> 6: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.90
#> 7: dCatch 2021 mean(C[t] / C[t-1]) 9.516750e-01 0.95
#> 8: varCatch 2021 var(C) 5.537231e+06 0.05
#> 9: varCatch 2021 var(C) 5.537231e+06 0.10
#> 10: varCatch 2021 var(C) 5.537231e+06 0.25
#> 11: varCatch 2021 var(C) 5.537231e+06 0.50
#> 12: varCatch 2021 var(C) 5.537231e+06 0.75
#> 13: varCatch 2021 var(C) 5.537231e+06 0.90
#> 14: varCatch 2021 var(C) 5.537231e+06 0.95
#> 15: varF 2021 var(F) 4.429523e-03 0.05
#> 16: varF 2021 var(F) 5.405319e-03 0.10
#> 17: varF 2021 var(F) 7.461941e-03 0.25
#> 18: varF 2021 var(F) 9.273349e-03 0.50
#> 19: varF 2021 var(F) 1.140404e-02 0.75
#> 20: varF 2021 var(F) 1.303102e-02 0.90
#> 21: varF 2021 var(F) 1.449680e-02 0.95
#> statistic year name data prob
# 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))]
#> CMSY
#> 1: 0.1193508
perf <- performance(FLStocks(B=run, A=run), statistics, refpts=FLPar(MSY=110000),
metrics=list(C=catch), years=list(2000:2015))
#> Error in .local(x, ...): years must be present in input object 'x' dimensions.