| Title: | Reference point computation for advice rules |
|---|---|
| Description: | Blah |
| Authors: | Henning Winker [aut, cre] |
| Maintainer: | Henning Winker <[email protected]> |
| License: | EUPL |
| Version: | 1.10.7 |
| Built: | 2024-09-07 05:36:31 UTC |
| Source: | https://github.com/Henning-Winker/FLRef |
ABItgt() Computes ABI for target F, e.g. ABImsy (Griffith et al. 2023)
ABItgt(stock, ftgt = 0.2, thresh = 0.9, ...)ABItgt(stock, ftgt = 0.2, thresh = 0.9, ...)
stock |
object of class FLStock |
ftgt |
target F at equilibrium, e.g. Fmsy |
thresh |
quantile ageref treshold, default 0.9 |
*FLQuant*
data(ple4) ABImsy = ABItgt(ple4,ftgt=0.22,thresh=0.9) plot(ABImsy)+ylim(0,2)+ geom_hline(yintercept = c(0.8,1),col=c(2,1),linetype=c(2,1))+ylab(expression(ABI[MSY]))data(ple4) ABImsy = ABItgt(ple4,ftgt=0.22,thresh=0.9) plot(ABImsy)+ylim(0,2)+ geom_hline(yintercept = c(0.8,1),col=c(2,1),linetype=c(2,1))+ylab(expression(ABI[MSY]))
ALK function
ALK(N_a, iALK)ALK(N_a, iALK)
N_a |
numbers at age sample for single event |
iALK |
from iALK() outout |
FLPar of ALK
generates annual ALK sample with length stratified sampling
alk.sample(lfds, alks, nbin = 20, n.sample = 1)alk.sample(lfds, alks, nbin = 20, n.sample = 1)
lfds |
length frequency *FLQuant* |
alks |
annual ALK proportions at age output form ALKs() *FLPars* |
nbin |
number of samples per length bin |
n.sample |
sample size of lfd |
FLPars of sampled ALK
annual ALK function
ALKs(object, iALK)ALKs(object, iALK)
object |
FLQuant with numbers at age |
iALK |
from iALK() outout |
FLPars of ALK
applyALK function to length to age
applyALK(lfds, alks)applyALK(lfds, alks)
alks |
*FLPars* annual ALKs |
lfd |
*FLQuant* with numbers at length |
FLQuant for numbers at age
asem2spm()
asem2spm( object, quant = c("vb", "ssb"), fmsy = NULL, rel = FALSE, spcurve = FALSE )asem2spm( object, quant = c("vb", "ssb"), fmsy = NULL, rel = FALSE, spcurve = FALSE )
object |
An *FLBRP* |
quant |
choose between vb and ssb |
rel |
if TRUE ratios are produced for spcurve |
spcurve |
if TRUE a data.frame is added |
prior means for r and m *FLPar*
data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) asem2spm(brp)[1:4] plotpf(brp) plotpf(brp,rel=TRUE)data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) asem2spm(brp)[1:4] plotpf(brp) plotpf(brp,rel=TRUE)
generates FLIndexBiomass with random observation error from an FLStock
bioidx.sim(object, sel = catch.sel(object), sigma = 0.2, q = 0.001)bioidx.sim(object, sel = catch.sel(object), sigma = 0.2, q = 0.001)
object |
FLStock |
sel |
FLQuant with selectivity.pattern |
sigma |
observation error for log(index) |
q |
catchability coefficient for scaling |
FLIndexBiomass
data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=1.5,S95=2.1,Smax=4.5,Dcv=1,Dmin=0.1)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") object = propagate(ple4,10) sel = newselex(catch.sel(object),FLPar(S50=2.5,S95=3.2,Smax=3.5,Dcv=0.6,Dmin=0.2)) idx = bioidx.sim(object,sel=sel,q=0.0001) # Checks ggplot([email protected])+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") ggplot(idx@index)+geom_line(aes(year,data,col=ac(iter)))+theme(legend.position = "none")+ylab("Index")data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=1.5,S95=2.1,Smax=4.5,Dcv=1,Dmin=0.1)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") object = propagate(ple4,10) sel = newselex(catch.sel(object),FLPar(S50=2.5,S95=3.2,Smax=3.5,Dcv=0.6,Dmin=0.2)) idx = bioidx.sim(object,sel=sel,q=0.0001) # Checks ggplot(idx@sel.pattern)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") ggplot(idx@index)+geom_line(aes(year,data,col=ac(iter)))+theme(legend.position = "none")+ylab("Index")
function to assign B[y+1] to B[y]. Warning correlation structure of B[y+1] and F[y] is meaningless
blag(mvn, verbose = TRUE)blag(mvn, verbose = TRUE)
mvn |
output list of quant posteriors and mle's
Henning Winker (GFCM)
generates catch.n with lognormal annual and multinomial age composition observation error
ca.sim(object, ess = 200, what = c("catch", "landings", "discards")[1])ca.sim(object, ess = 200, what = c("catch", "landings", "discards")[1])
object |
FLQuant |
ess |
effective sample size for age composition |
what |
c("catch", "landings", "discards") |
sel |
FLQuant with selectivity.pattern e.g. catch.sel() |
FLQuant with catch.n samples
data(ple4) object = propagate(ple4,10) ca = ca.sim(object,ess=200) # Checks ggplot(ca)+geom_line(aes(year,data,col=ac(iter)))+facet_wrap(~age)+ theme(legend.position = "none")+ylab("Index")data(ple4) object = propagate(ple4,10) ca = ca.sim(object,ess=200) # Checks ggplot(ca)+geom_line(aes(year,data,col=ac(iter)))+facet_wrap(~age)+ theme(legend.position = "none")+ylab("Index")
computeFbrp() Computes biological reference points corresponding to the proxy Fbrp
computeFbrp( stock, sr = "missing", proxy = NULL, x = NULL, blim = 0.1, type = c("b0", "btgt", "value"), btri = "missing", bpa = "missing", bthresh = "missing", verbose = T, fmax = 10, ... )computeFbrp( stock, sr = "missing", proxy = NULL, x = NULL, blim = 0.1, type = c("b0", "btgt", "value"), btri = "missing", bpa = "missing", bthresh = "missing", verbose = T, fmax = 10, ... )
stock |
object of class FLStock |
sr |
stock recruitment model of class FLSR |
proxy |
choice of Fmsy proxies (combinations permitted)
|
x |
basis in percent for sprx and bx, e.g. 40 for spr40 |
blim |
values < 1 are taken as fraction to B0 and blim > 1 as absolute values unless specified otherwise |
type |
type of blim input, values < 1 are
|
btri |
Btrigger can specified as absolute value |
bpa |
Bpa can specified as absolute value |
bthresh |
Bthresh (GFCM) interchangeable use with Bpa |
verbose |
|
fmax |
maximum Flim = max(Flim,fmax*Fbrp) |
brp object of class FLBRP with computed Fbrp reference points
data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1"),blim=0.1,type="b0") ploteq(brp,obs=TRUE,refpts="msy")data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1"),blim=0.1,type="b0") ploteq(brp,obs=TRUE,refpts="msy")
computeFbrps() Computes biological reference points corresponding to the proxy Fbrp
computeFbrps( stock, sr = "missing", proxy = c("sprx", "bx", "all"), fmsy = FALSE, f0.1 = TRUE, fmax = 5, verbose = T, ... )computeFbrps( stock, sr = "missing", proxy = c("sprx", "bx", "all"), fmsy = FALSE, f0.1 = TRUE, fmax = 5, verbose = T, ... )
stock |
object of class FLStock |
sr |
stock recruitment model of class FLSR |
fmsy |
if TRUE, Fmsy is computed (not suggest for segreg or geomean sr) |
f0.1 |
if TRUE, F0.1 is computed |
fmax |
maximum Flim = minfmax*Fbrp) |
verbose |
|
proxies |
choice of Fmsy proxies
|
brp object of class FLBRP with computed Fbrp reference points
Fbrp() Extract Fbrp based reference points from output of computeFbrp
Fbrp(brp)Fbrp(brp)
brp |
input of class FLBRP from ComputeFbrp |
FLPar object with computed Fbrp reference points
Fe40() Patterson estimator for Fmsy
Fe40(stock, nyears = 3)Fe40(stock, nyears = 3)
stock |
input of class FLStock |
nyears |
number of years to average |
value
flr2stars()
flr2stars(object, quantiles = c(0.05, 0.95))flr2stars(object, quantiles = c(0.05, 0.95))
object |
of class FLStockR |
quantities |
default is 90CIs as c(0.05,0.95) |
STARS list with $timeseris and $refpts
Fmmy() Uses opt.bisect to derive the F at Maximum Median Yield from stochastic simulations
Fmmy( brp, sigmaR = 0.5, rho = 0, nyears = 100, iters = 250, yrs.eval = NULL, range = "missing", tol = 0.001, maxit = 15, verbose = TRUE )Fmmy( brp, sigmaR = 0.5, rho = 0, nyears = 100, iters = 250, yrs.eval = NULL, range = "missing", tol = 0.001, maxit = 15, verbose = TRUE )
brp |
output object from computeFbrp() of class FLBRP |
sigmaR |
lognormal recruitment standard deviation |
rho |
AR1 recruitment autocorrelation coefficient |
nyears |
number of simulation years |
iters |
number simulation iterations |
yrs.eval |
last years to be used evaluation period, default nyears/2 |
range |
range of Fbar value to be evaluated |
tol |
tolerance |
maxit |
number of steps |
verbose |
cat comments |
list of FLPar, FLStock and FLBRP objects
data(ple4) bh = srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4)) brp = computeFbrp(ple4,bh,proxy=c("bx","msy"),x=35,blim=0.1) fmmy = Fmmy(brp,sigmaR=0.7,rho=0.3) getF(fmmy) # FMMY value plotFsim(fmmy) brpfmmy = computeFbrp(ple4,bh,proxy=getF(fmmy),blim=0.1) fsim = Fsim(brpfmmy,sigmaR=0.7,rho=0.3) plotFsim(fsim)data(ple4) bh = srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4)) brp = computeFbrp(ple4,bh,proxy=c("bx","msy"),x=35,blim=0.1) fmmy = Fmmy(brp,sigmaR=0.7,rho=0.3) getF(fmmy) # FMMY value plotFsim(fmmy) brpfmmy = computeFbrp(ple4,bh,proxy=getF(fmmy),blim=0.1) fsim = Fsim(brpfmmy,sigmaR=0.7,rho=0.3) plotFsim(fsim)
Calculates the Fbar value giving a maximum probability of ssb being below Blim of 5 percent
Fp05( object, iters = "missing", range = "missing", tol = 0.001, maxit = 20, verbose = TRUE )Fp05( object, iters = "missing", range = "missing", tol = 0.001, maxit = 20, verbose = TRUE )
object |
output from Fsim() |
iters |
Number of iterations, cannot exceed input object |
range |
range of Fbar value to be evaluated |
verbose |
Should progress be shown, TRUE. |
list
data(ple4) bh = srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4)) brp = computeFbrp(ple4,bh,proxy="bx",x=35,blim=0.2) # set Blim higher fsim = Fsim(brp,sigmaR=0.7,rho=0.3,iters=500) plotFsim(fsim) fp.05 = Fp05(fsim) plotFsim(fp.05,panels=c(2,4)) # black line is Fp0.05 getF(fp.05)data(ple4) bh = srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4)) brp = computeFbrp(ple4,bh,proxy="bx",x=35,blim=0.2) # set Blim higher fsim = Fsim(brp,sigmaR=0.7,rho=0.3,iters=500) plotFsim(fsim) fp.05 = Fp05(fsim) plotFsim(fp.05,panels=c(2,4)) # black line is Fp0.05 getF(fp.05)
Fsim() Simulates stochastic stock dynamics under under constant Fbrp
Fsim( brp, Ftgt = NULL, sigmaR = 0.5, rho = 0, nyears = 100, iters = 250, yrs.eval = NULL, verbose = TRUE )Fsim( brp, Ftgt = NULL, sigmaR = 0.5, rho = 0, nyears = 100, iters = 250, yrs.eval = NULL, verbose = TRUE )
brp |
output object from computeFbrp() of class FLBRP |
sigmaR |
lognormal recruitment standard deviation |
rho |
AR1 recruitment autocorrelation coefficient |
nyears |
number of simulation years |
iters |
number simulation iterations |
yrs.eval |
last years to be used evaluation period, default nyears/2 |
verbose |
cat comments |
list of FLPar, FLStock and FLBRP objects
data(ple4) hs = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4),lplim=0.05,uplim=0.25) blim = params(hs)[[2]] brp = computeFbrp(ple4,hs,proxy=c("sprx","f0.1","msy"),x=40,blim=blim) ploteq(brp) fsim = Fsim(brp,sigmaR=0.7,rho=0.3) plotFsim(fsim) plotFsim(fsim,panels=2)data(ple4) hs = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4),lplim=0.05,uplim=0.25) blim = params(hs)[[2]] brp = computeFbrp(ple4,hs,proxy=c("sprx","f0.1","msy"),x=40,blim=blim) ploteq(brp) fsim = Fsim(brp,sigmaR=0.7,rho=0.3) plotFsim(fsim) plotFsim(fsim,panels=2)
generates an up-down-constant F-pattern
fudc( object, fref = 0.2, fhi = 2.5, flo = 0.8, sigmaF = 0.2, breaks = c(0.5, 0.75) )fudc( object, fref = 0.2, fhi = 2.5, flo = 0.8, sigmaF = 0.2, breaks = c(0.5, 0.75) )
object |
An *FLStock* |
fref |
reference denominator for fbar |
fhi |
factor for high F as fhi = fbar/fref |
flo |
factor for low F as flo = fbar/fref |
sigmaF |
variation on fbar |
breaks |
relative location of directional change |
FLQuant
data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fmsy = Fbrp(brp)["Fmsy"] stki = propagate(ple4,100) fy = fudc(ple4,fhi=2,flo=0.9,fref=fmsy,sigmaF=0) fyi = fudc(stki,fhi=2,flo=0.9,fref=fmsy,sigmaF=0.2) plot(fy,fyi)+ylab("F") #Forcasting om <- FLStockR(ffwd(stki,sr,fbar=fyi)) om@refpts = Fbrp(brp) plotAdvice(window(om,start=1960))data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fmsy = Fbrp(brp)["Fmsy"] stki = propagate(ple4,100) fy = fudc(ple4,fhi=2,flo=0.9,fref=fmsy,sigmaF=0) fyi = fudc(stki,fhi=2,flo=0.9,fref=fmsy,sigmaF=0.2) plot(fy,fyi)+ylab("F") #Forcasting om <- FLStockR(ffwd(stki,sr,fbar=fyi)) om@refpts = Fbrp(brp) plotAdvice(window(om,start=1960))
Function to summarise forecast results
fwd2stars(object, eval.yrs = NULL, rel = TRUE)fwd2stars(object, eval.yrs = NULL, rel = TRUE)
object |
*FLStocks* with list of *FLStockR* objects |
eval.yrs |
evaluation years of forecast |
rel |
if TRUE ratios B/Btgt and F/Ftgt are shown |
data.frame
Helper functio to extract F from various FLRef output
getF(x)getF(x)
x |
output object from computeFbrp() of class FLBRP |
huecol
huecol(n, alpha = 1)huecol(n, alpha = 1)
n |
number of colors |
alpha |
transluscency |
inverse ALK function with lmin added to FLCore::invALK
iALK( params, model = vonbert, age, cv = 0.1, lmin = 5, lmax = 1.2, bin = 1, max = ceiling(linf * lmax), reflen = NULL )iALK( params, model = vonbert, age, cv = 0.1, lmin = 5, lmax = 1.2, bin = 1, max = ceiling(linf * lmax), reflen = NULL )
params |
growth parameter, default FLPar(linf,k,t0) |
model |
growth model, only option currently vonbert |
age |
age vector |
cv |
of length-at-age |
lmin |
minimum length |
lmax |
maximum upper length specified lmax*linf |
bin |
length bin size, dafault 1 |
max |
maximum size value |
reflen |
evokes fixed sd for L_a at sd = cv*reflen |
timing |
t0 assumed 1st January, default seq(0,11/12,1/12), but can be single event 0.5 |
unit |
default is "cm" |
FLPar age-length matrix
generates FLIndex with lognormal annual and multinomial age composition observation error
idx.sim( object, sel = catch.sel(object), ages = NULL, years = NULL, ess = 200, sigma = 0.2, q = 0.01 )idx.sim( object, sel = catch.sel(object), ages = NULL, years = NULL, ess = 200, sigma = 0.2, q = 0.01 )
object |
FLStock |
sel |
FLQuant with selectivity.pattern |
ages |
define age range |
years |
define year range |
ess |
effective sample size for age composition sample |
sigma |
annual observation error for log(q) |
q |
catchability coefficient for scaling |
FLIndex
data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=1.5,S95=2.1,Smax=4.5,Dcv=1,Dmin=0.1)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") object = propagate(ple4,10) idx = idx.sim(object,sel=sel,ess=200,sigma=0.2,q=0.01,years=1994:2017) # Checks ggplot([email protected])+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") ggplot(idx@index)+geom_line(aes(year,data,col=ac(iter)))+facet_wrap(~age,scales="free_y")+ theme(legend.position = "none")+ylab("Index")data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=1.5,S95=2.1,Smax=4.5,Dcv=1,Dmin=0.1)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") object = propagate(ple4,10) idx = idx.sim(object,sel=sel,ess=200,sigma=0.2,q=0.01,years=1994:2017) # Checks ggplot(idx@sel.pattern)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") ggplot(idx@index)+geom_line(aes(year,data,col=ac(iter)))+facet_wrap(~age,scales="free_y")+ theme(legend.position = "none")+ylab("Index")
jabba2FLStockR()
jabba2FLStockR(jabba, blim = 0.3, bthr = 0.5, thin = 10, rel = FALSE)jabba2FLStockR(jabba, blim = 0.3, bthr = 0.5, thin = 10, rel = FALSE)
jabba |
fit from JABBA fit_jabba() or jabba$kbtrj |
blim |
biomass limit reference point as fraction of Bmsy |
thin |
thinnig rate of retained iters |
rel |
if TRUE ratios BBmsy and FFmsy are stored |
bpa |
biomass precautionary reference point as fraction of Bmsy |
FLStockR with refpts
jabba2stars()
jabba2stars(jabba, quantiles = c(0.05, 0.95), blim = 0.3, bthr = 0.5)jabba2stars(jabba, quantiles = c(0.05, 0.95), blim = 0.3, bthr = 0.5)
jabba |
fit from JABBA fit_jabba() or jabba$kbtrj |
quantiles |
default is 90CIs as c(0.05,0.95) |
blim |
biomass limit point as fraction of Bmsy, default 0.3Bmsy (ICES) |
bthr |
biomass precautionary point as fraction of Bmsy, default 0.5Bmsy (ICES) |
STARS list with $timeseris and $refpts
function to generate survey (pulse) and continuous LFDs
len.sim( N_a, params, model = vonbert, ess = 250, timing = seq(0, 11/12, 1/12), unit = "cm", scale = TRUE, reflen = NULL, bin = 1, cv = 0.1, lmin = 5, lmax = 1.2 )len.sim( N_a, params, model = vonbert, ess = 250, timing = seq(0, 11/12, 1/12), unit = "cm", scale = TRUE, reflen = NULL, bin = 1, cv = 0.1, lmin = 5, lmax = 1.2 )
N_a |
numbers at age sample |
params |
growth parameter, default FLPar(linf,k,t0) |
model |
growth model, only option currently vonbert |
ess |
effective sample size |
timing |
t0 assumed 1st January, default seq(0,11/12,1/12), but can be single event 0.5 |
unit |
default is "cm" |
scale |
if TRUE scaled to N_a input |
reflen |
evokes fixed sd for L_a at sd = cv*reflen |
bin |
length bin size, dafault 1 |
cv |
variation in L_a |
lmin |
minimum length |
lmax |
maximum upper length specified lmax*linf |
FLQuant for length
function to generate survey (pulse) and continuous LFDs
lfd.sim( object, stock, sel = catch.sel(stock), params, model = vonbert, ess = 250, timing = seq(0, 11/12, 1/12), timeref = 0.5, unit = "cm", scale = TRUE, reflen = NULL, bin = 1, cv = 0.1, lmin = 5, lmax = 1.2 )lfd.sim( object, stock, sel = catch.sel(stock), params, model = vonbert, ess = 250, timing = seq(0, 11/12, 1/12), timeref = 0.5, unit = "cm", scale = TRUE, reflen = NULL, bin = 1, cv = 0.1, lmin = 5, lmax = 1.2 )
object |
*FLQuant* numbers at age sample |
stock |
*FLStock* object |
sel |
selectivity, default catch.sel(stock) |
params |
growth parameter, default FLPar(linf,k,t0) |
model |
growth model, only option currently vonbert |
ess |
effective sample size |
timing |
default constinoues seq(0,11/12,1/12), but can be single event 0.5 |
timeref |
reference timing of the sample, default 0.5 (e.g. survey or catch.n) |
unit |
default is "cm" |
scale |
if TRUE scaled to N_a input |
reflen |
evokes fixed sd for L_a at sd = cv*reflen |
bin |
length bin size, dafault 1 |
cv |
variation in L_a |
lmin |
minimum length |
lmax |
maximum upper length specified lmax*linf |
FLQuant for length
computes Lorenzen M with scaling option
Mlorenzen(object, Mref = "missing", Aref = 2)Mlorenzen(object, Mref = "missing", Aref = 2)
object |
weight-at-age of class *FLQuant* |
Mref |
reference M for scaling |
Aref |
reference Age for scaling |
FLQuant m()
data(ple4) Ml = Mlorenzen(stock.wt(ple4)) # Scale Ms = Mlorenzen(stock.wt(ple4),Mref=0.2,Aref=2) flqs = FLQuants(Lorenzen=Ml,Scaled=Ms)data(ple4) Ml = Mlorenzen(stock.wt(ple4)) # Scale Ms = Mlorenzen(stock.wt(ple4),Mref=0.2,Aref=2) flqs = FLQuants(Lorenzen=Ml,Scaled=Ms)
generates flexible 5-paramater selex curves
newselex(object, selexpars)newselex(object, selexpars)
object |
FLQuant from catch.sel() or sel.pattern() |
selexpars |
Selectivity Parameters selexpars S50, S95, Smax, Dcv, Dmin
|
FLquant with selectivity pattern
data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=2,S95=3,Smax=4.5,Dcv=0.6,Dmin=0.3)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") # Simulate harvest(ple4)[] = sel sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fbar(brp) = FLQuant(rep(0.01,70)) stk = as(brp,"FLStock") units(stk) = standardUnits(stk) its = 100 stk <- FLStockR(propagate(stk, its)) stk@refpts= Fbrp(brp) b0=an(Fbrp(brp)["B0"]) control = FLPar(Feq=0.15,Frate=0.1,Fsigma=0.15,SB0=b0,minyear=2,maxyear=70,its=its) run <- rffwd(stk, sr=sr,control=control,deviances=ar1rlnorm(0.3, 1:70, its, 0, 0.6)) plotAdvice(run)data(ple4) sel = newselex(catch.sel(ple4),FLPar(S50=2,S95=3,Smax=4.5,Dcv=0.6,Dmin=0.3)) ggplot(sel)+geom_line(aes(age,data))+ylab("Selectivity")+xlab("Age") # Simulate harvest(ple4)[] = sel sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fbar(brp) = FLQuant(rep(0.01,70)) stk = as(brp,"FLStock") units(stk) = standardUnits(stk) its = 100 stk <- FLStockR(propagate(stk, its)) stk@refpts= Fbrp(brp) b0=an(Fbrp(brp)["B0"]) control = FLPar(Feq=0.15,Frate=0.1,Fsigma=0.15,SB0=b0,minyear=2,maxyear=70,its=its) run <- rffwd(stk, sr=sr,control=control,deviances=ar1rlnorm(0.3, 1:70, its, 0, 0.6)) plotAdvice(run)
The plain bisection algorithm (Burden & Douglas, 1985) is employed here to find the value of a given forecast target quantity (e.g. 'fbar') for which a selected value of a performance statistic is obtained over a chosen period.
opt.bisect( stock, sr, deviances = rec(stock) %=% 1, metrics, statistic, years, pyears = years, tune, tol = 0.001, maxit = 15, log = TRUE, verbose = TRUE )opt.bisect( stock, sr, deviances = rec(stock) %=% 1, metrics, statistic, years, pyears = years, tune, tol = 0.001, maxit = 15, log = TRUE, verbose = TRUE )
stock |
object class FLStock |
sr |
object class FLSR |
metrics |
FLQuant of FLStock to be defined |
statistic |
|
years |
years to be evaluated |
tune |
range for input x |
tol |
tolerance level |
maxit |
number of optimisation steps |
log |
if TRUE, optimise on log-scale |
Credits to Iago Mosqueira
Burden, Richard L.; Faires, J. Douglas (1985), "2.1 The Bisection Algorithm", Numerical Analysis (3rd ed.), PWS Publishers, ISBN 0-87150-857-5
data(ple4) stock <- propagate(stf(ple4, end=2118), 200) srr <- predictModel(model=rec ~ ifelse(ssb <= b, a * ssb, a * b), params=FLPar(a=1.29, b=1.35e+06)) # GENERATE SRR deviances devs <- ar1rlnorm(rho=0.4, 2018:2118, iters=200, meanlog=0, sdlog=0.5) # DEFINE MMY statistic statistic <- list(MMY=list(~apply(L,1,median), name="MMY", desc="ICES Maximum Median Yield")) # CALL bisect over 100 years, Fmmy calculated over last 50. fmmy <- opt.bisect(stock, sr=srr, deviances=devs, metrics=list(L=landings), statistic=statistic, years=2018:2118, pyears=2069:2118, tune=list(fbar=c(0.01, 0.2))) # fmmy mean(fbar(fmmy)[,ac(2069:2118)])data(ple4) stock <- propagate(stf(ple4, end=2118), 200) srr <- predictModel(model=rec ~ ifelse(ssb <= b, a * ssb, a * b), params=FLPar(a=1.29, b=1.35e+06)) # GENERATE SRR deviances devs <- ar1rlnorm(rho=0.4, 2018:2118, iters=200, meanlog=0, sdlog=0.5) # DEFINE MMY statistic statistic <- list(MMY=list(~apply(L,1,median), name="MMY", desc="ICES Maximum Median Yield")) # CALL bisect over 100 years, Fmmy calculated over last 50. fmmy <- opt.bisect(stock, sr=srr, deviances=devs, metrics=list(L=landings), statistic=statistic, years=2018:2118, pyears=2069:2118, tune=list(fbar=c(0.01, 0.2))) # fmmy mean(fbar(fmmy)[,ac(2069:2118)])
sets plus group on FLQuant
pgquant(object, pg)pgquant(object, pg)
object |
FLQuant |
pg |
FLQuant
plotAdvice Plots stochastic stock dynamics against refpts for constant Fsim()
plotAdvice( object, rpts = "missing", type = NULL, plotrefs = TRUE, probs = c(0.05, 0.2, 0.5, 0.8, 0.95), colour = "dodgerblue", ncol = NULL, label.size = 2.5 )plotAdvice( object, rpts = "missing", type = NULL, plotrefs = TRUE, probs = c(0.05, 0.2, 0.5, 0.8, 0.95), colour = "dodgerblue", ncol = NULL, label.size = 2.5 )
type |
age-structured "asm" or surplus production "spm" plotting style |
plotrefs |
if TRUE reference points are plotted |
probs |
determine credibility intervals, default 80th, 90th percentiles #' @param ncol number of plot panel columns |
colour |
color of CIs |
label.size |
size of refpts labels |
stock |
FLStock or FLStockR |
refpts |
as FLPar or Fbrp() if FLStockR is not provided or should be overwritten |
ggplot
data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1","fe40"),blim=0.1,type="b0") plotAdvice (ple4,brp)data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1","fe40"),blim=0.1,type="b0") plotAdvice (ple4,brp)
plotAR Plots the new proposed ICES advice rule
plotAR( pars, ftgt = 1, btrigger = "missing", bpa = "missing", bthresh = "missing", fpa = "missing", fthresh = "missing", bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.2, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, label.cex = 3.5, critical = TRUE )plotAR( pars, ftgt = 1, btrigger = "missing", bpa = "missing", bthresh = "missing", fpa = "missing", fthresh = "missing", bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.2, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, label.cex = 3.5, critical = TRUE )
pars |
FLPar object or computeFbrp() ouput
|
ftgt |
factor to adjust Fmsy or its proxy e.g. 0.8Fmsy |
btrigger |
biomass trigger below which F is linearly reduced, if > 10 value, else factor*Btgt |
bpa |
precautionary biomass threshold, if > 10 value, else factor*Blim |
fpa |
option to input Fpa value |
bclose |
biomass that invokes fishing closure |
fmin |
minimum allowable (bycatch) fishing mortality under closure |
obs |
obtion to show observation with input class 'FLStock' |
kobe |
add kobe colour-coding |
alpha |
transparency of shading |
xmax |
multiplier for upper default xlim |
ymax |
multiplier for upper default ylim |
ylab |
option customize ylab |
xlab |
option customize xlab |
rel |
option to denote x,y labs as relative B/Btgt and F/Ftgt |
expand |
option to expand the plot area to border - default TRUE |
labels |
annotate reference point labels |
critical |
option to highlight critical zone below blim |
labelslabel.cex=3.5 |
set size of labels |
ggplot
data(ple4) srr = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4)) blim = params(srr)[[2]] brp = computeFbrp(stock=ple4,sr=srr,proxy="f0.1",blim=blim) rpt = Fbrp(brp) plotAR(rpt,btrigger=an(0.8*rpt["Btgt"])) # Use Bpa as trigger (ICES style) plotAR(rpt,obs=ple4,bpa=1.4) # Change kobe to greyscale plotAR(rpt,obs=ple4,bpa=1.4,kobe=FALSE) # add fishing closure with minimum unavoidable F and Btrigger plotAR(rpt,obs=ple4,bpa=1.4,btrigger=0.7,kobe=TRUE,bclose=1,fmin=0.01) # show a relative plotAR(rpt,obs=ple4,rel=TRUE,bpa=1.4,btrigger=0.7,kobe=TRUE,bclose=1,fmin=0.02)data(ple4) srr = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4)) blim = params(srr)[[2]] brp = computeFbrp(stock=ple4,sr=srr,proxy="f0.1",blim=blim) rpt = Fbrp(brp) plotAR(rpt,btrigger=an(0.8*rpt["Btgt"])) # Use Bpa as trigger (ICES style) plotAR(rpt,obs=ple4,bpa=1.4) # Change kobe to greyscale plotAR(rpt,obs=ple4,bpa=1.4,kobe=FALSE) # add fishing closure with minimum unavoidable F and Btrigger plotAR(rpt,obs=ple4,bpa=1.4,btrigger=0.7,kobe=TRUE,bclose=1,fmin=0.01) # show a relative plotAR(rpt,obs=ple4,rel=TRUE,bpa=1.4,btrigger=0.7,kobe=TRUE,bclose=1,fmin=0.02)
plotbioage() Plots stock N_a, W_a, M_a and Mat_a by year
plotbioage(stk, ncol = 2)plotbioage(stk, ncol = 2)
stk |
stock object class FLStock |
ncol |
number of columns in multiplot |
ggplot
data(ple4) plotbioage(ple4)data(ple4) plotbioage(ple4)
plotbioyr() Plots stock N_a, W_a, M_a and Mat_a across years
plotbioyr(stk, ncol = 2)plotbioyr(stk, ncol = 2)
stk |
stock object class FLStock |
ncol |
number of columns in multiplot |
ggplot
data(ple4) plotbioyr(ple4)data(ple4) plotbioyr(ple4)
plotdyn() Plots stock trajectories at age
plotdyn(stk, ncol = 2)plotdyn(stk, ncol = 2)
stk |
stock object class FLStock |
ncol |
number of columns in multiplot |
ggplot
data(ple4) plotdyn(ple4)data(ple4) plotdyn(ple4)
ploteq() Modification of method plot('FLBRP') to plot equilibrium output of computeFbrp()
ploteq( brps, refpts = "missing", obs = FALSE, rel = FALSE, rpf = TRUE, dashed = rpf, colours = "missing", panels = NULL, ncol = 2 )ploteq( brps, refpts = "missing", obs = FALSE, rel = FALSE, rpf = TRUE, dashed = rpf, colours = "missing", panels = NULL, ncol = 2 )
brps |
output object from computeFbrp of class FLBRP |
refpts |
Reference points, defaults are computed refpts from computeFbrp()
|
obs |
Should observations be plotted? Defaults to 'FALSE'. |
rel |
option to denote x,y labs as relative B/Btgt and F/Ftgt |
rpf |
adds refpts in plots |
dashed |
plots vertical dashed lines to highlight refpts locations |
colours |
refpts colours, default is designed for computeFbrp() output |
panels |
plot panel option 1:4 |
ncol |
number of plot panel columns |
ggplot
data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1","msy"),blim=0.1,type="b0") ploteq(brp,obs=TRUE) ploteq(brp,obs=TRUE,refpts="msy",rel=TRUE) brp.pa = computeFbrp(stock=ple4,sr=srr,proxy=c("msy","sprx","f0.1"),blim=0.1,bpa=Fbrp(brp)["Blim"]*2,type="b0") ploteq(brp.pa,obs=TRUE,rel=TRUE)data(ple4) srr = srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4)) brp = computeFbrp(stock=ple4,sr=srr,proxy=c("sprx","f0.1","msy"),blim=0.1,type="b0") ploteq(brp,obs=TRUE) ploteq(brp,obs=TRUE,refpts="msy",rel=TRUE) brp.pa = computeFbrp(stock=ple4,sr=srr,proxy=c("msy","sprx","f0.1"),blim=0.1,bpa=Fbrp(brp)["Blim"]*2,type="b0") ploteq(brp.pa,obs=TRUE,rel=TRUE)
plotFsim Plots stochastic stock dynamics against refpts for constant Fsim()
plotFsim( object, worms = TRUE, thinning = 10, probs = c(0.05, 0.2, 0.5, 0.8, 0.95), plotrefs = TRUE, colour = "missing", ncol = "missing", label.size = 3, yrs.eval = NULL, panels = "missing" )plotFsim( object, worms = TRUE, thinning = 10, probs = c(0.05, 0.2, 0.5, 0.8, 0.95), plotrefs = TRUE, colour = "missing", ncol = "missing", label.size = 3, yrs.eval = NULL, panels = "missing" )
object |
output object from Fsim() |
worms |
option to show individual iterations |
thinning |
thinning rate of iterations shows, e.g. 10 shows every 10th |
probs |
determine credibility intervals, default 80th, 90th percentiles |
plotrefs |
if TRUE reference points are plotted |
colour |
color of CIs |
ncol |
number of plot panel columns |
label.size |
size of reference points |
yrs.eval |
last years to be used evaluation period, default half nyears |
ggplot
Plots the new proposed ICES advice rule
plotMajuro( ftgt = 1, fthresh = 1.1, btgt = 1, blim = 0.1, btrigger = 0.8 * btgt, bthresh = 0.5 * btgt, bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.5, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, critical = kobe )plotMajuro( ftgt = 1, fthresh = 1.1, btgt = 1, blim = 0.1, btrigger = 0.8 * btgt, bthresh = 0.5 * btgt, bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.5, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, critical = kobe )
ftgt |
Target F = min(Fbrp,Fp0.5) |
btgt |
Biomass target corresponding to Fbrp |
blim |
biomass limit |
btrigger |
biomass trigger below which F is linearly reduced |
bthresh |
biomass threshold beyond which biomass is classified sustainable |
bclose |
biomass that invokes fishing closure |
fmin |
minimum allowable (bycatch) fishing mortality under closure |
obs |
obtion to show observation with input class 'FLStock' |
kobe |
add kobe colour-coding |
alpha |
transparency of shading |
xmax |
multiplier for upper default xlim |
ymax |
multiplier for upper default ylim |
ylab |
option customize ylab |
xlab |
option customize xlab |
rel |
option to denote x,y labs as relative B/Btgt and F/Ftgt |
expand |
option to expand the plot area to border - default TRUE |
labels |
annotate reference point labels |
critical |
option to highlight critical zone below blim |
ggplot
plotMajuro()plotMajuro()
plots production functions
plotpf(object, quant = c("vb", "ssb"), fmsy = NULL, rel = FALSE)plotpf(object, quant = c("vb", "ssb"), fmsy = NULL, rel = FALSE)
object |
An *FLBRP* |
quant |
choose between vb and ssb or both |
fmsy |
default if Fmsy |
rel |
if TRUE ratios are produced for spcurve |
ggplot
data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) asem2spm(brp)[1:4] plotpf(brp) plotpf(brp,rel=TRUE)data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) asem2spm(brp)[1:4] plotpf(brp) plotpf(brp,rel=TRUE)
plotspr() Plots current vs unfished spawning biomass per recruit at age
plotspr(stk, nyears = 3)plotspr(stk, nyears = 3)
stk |
stock object class FLStock |
nyears |
number of current last years, default is 3 |
ncol |
number of columns in multiplot |
ggplot
data(ple4) plotbioage(ple4)data(ple4) plotbioage(ple4)
plotWKREF Plots the new proposed ICES advice rule
plotWKREF( ftgt = 1, btgt = 1, blim = 0.2, btrigger = 0.9 * btgt, bthresh = 0.8 * btgt, bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.3, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, critical = kobe )plotWKREF( ftgt = 1, btgt = 1, blim = 0.2, btrigger = 0.9 * btgt, bthresh = 0.8 * btgt, bclose = 0, fmin = 0, obs = "missing", kobe = TRUE, alpha = 1, xmax = 1.3, ymax = 1.5, ylab = "missing", xlab = "missing", rel = FALSE, expand = TRUE, labels = TRUE, critical = kobe )
ftgt |
Target F = min(Fbrp,Fp0.5) |
btgt |
Biomass target corresponding to Fbrp |
blim |
biomass limit |
btrigger |
biomass trigger below which F is linearly reduced |
bthresh |
biomass threshold beyond which biomass is classified sustainable |
bclose |
ratio biomass/blim that invokes fishing closure relative to blim |
fmin |
minimum allowable (bycatch) fishing mortality under closure |
obs |
obtion to show observation with input class 'FLStock' |
kobe |
add kobe colour-coding |
alpha |
transparency of shading |
xmax |
multiplier for upper default xlim |
ymax |
multiplier for upper default ylim |
ylab |
option customize ylab |
xlab |
option customize xlab |
rel |
option to denote x,y labs as relative B/Btgt and F/Ftgt |
expand |
option to expand the plot area to border - default TRUE |
labels |
annotate reference point labels |
critical |
option to highlight critical zone below blim |
ggplot
plotWKREF() # Close fishery at Blim and adjust axis labels to relative plotWKREF(blim=0.2,bclose=0.2,rel=TRUE) # Close fishery at Blim, but allow fmin (e.g. bycatch) plotWKREF(blim=0.2,bclose=0.2,fmin=0.1,rel=TRUE) # Change Btrigger above Btgt plotWKREF(blim=0.2,bclose=0.2,fmin=0.1,btrigger=0.80,rel=TRUE) # Plot stock data data(ple4) plotWKREF(ftgt=0.25,btgt=8e+05,btrigger = 0.9*8e+05, blim=2e5,bclose=3e5,fmin=0.03,obs=ple4)plotWKREF() # Close fishery at Blim and adjust axis labels to relative plotWKREF(blim=0.2,bclose=0.2,rel=TRUE) # Close fishery at Blim, but allow fmin (e.g. bycatch) plotWKREF(blim=0.2,bclose=0.2,fmin=0.1,rel=TRUE) # Change Btrigger above Btgt plotWKREF(blim=0.2,bclose=0.2,fmin=0.1,btrigger=0.80,rel=TRUE) # Plot stock data data(ple4) plotWKREF(ftgt=0.25,btgt=8e+05,btrigger = 0.9*8e+05, blim=2e5,bclose=3e5,fmin=0.03,obs=ple4)
r4sscol
rc4(n, alpha = 1)rc4(n, alpha = 1)
n |
number of colors |
alpha |
transluscency |
vector of color codes
rffwd() Project forward an FLStock with evolutionary Fbar
rffwd(object, sr, fbar = control, control = fbar, deviances = "missing")rffwd(object, sr, fbar = control, control = fbar, deviances = "missing")
object |
An *FLStock* |
sr |
A stock-recruit relationship, *FLSR* or *predictModel*. |
fbar |
Yearly target for average fishing mortality, *FLQuant*. |
control |
Yearly target for average fishing mortality, *FLPar*. |
deviances |
Deviances for the strock-recruit relationsip, *FLQuant*. |
The projected *FLStock* object.
data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fbar(brp) = FLQuant(rep(0.01,70)) stk = as(brp,"FLStock") units(stk) = standardUnits(stk) its = 100 stk <- FLStockR(propagate(stk, its)) stk@refpts= Fbrp(brp) b0=an(Fbrp(brp)["B0"]) control = FLPar(Feq=0.15,Frate=0.1,Fsigma=0.15,SB0=b0,minyear=2,maxyear=70,its=its) run <- rffwd(stk, sr=sr,control=control,deviances=ar1rlnorm(0.3, 1:70, its, 0, 0.6)) plotAdvice(run)data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = computeFbrp(ple4,sr,proxy="msy") fbar(brp) = FLQuant(rep(0.01,70)) stk = as(brp,"FLStock") units(stk) = standardUnits(stk) its = 100 stk <- FLStockR(propagate(stk, its)) stk@refpts= Fbrp(brp) b0=an(Fbrp(brp)["B0"]) control = FLPar(Feq=0.15,Frate=0.1,Fsigma=0.15,SB0=b0,minyear=2,maxyear=70,its=its) run <- rffwd(stk, sr=sr,control=control,deviances=ar1rlnorm(0.3, 1:70, its, 0, 0.6)) plotAdvice(run)
Function to characterize Productivity and refpts based on r and Generation
rGclass(r = NULL, gt = NULL)rGclass(r = NULL, gt = NULL)
r |
value of the intrinsic rate of population increase |
gt |
generation time G |
list with Productivity category and suggest Fbrps
generates a Schafer surplus production model with process and observation error
schaefer.sim( k = 10000, r = 0.3, q = 0.5, pe = 0.1, oe = 0.2, bk = 0.9, years = 1980:2022, f0 = 0.2, fhi = 2.2, flo = 0.8, sigmaF = 0.15, iters = 1, blim = 0.3, bthr = 0.5, rel = FALSE )schaefer.sim( k = 10000, r = 0.3, q = 0.5, pe = 0.1, oe = 0.2, bk = 0.9, years = 1980:2022, f0 = 0.2, fhi = 2.2, flo = 0.8, sigmaF = 0.15, iters = 1, blim = 0.3, bthr = 0.5, rel = FALSE )
k |
carrying capacity |
r |
intrinsic rate of population increase |
q |
catchability coefficient |
pe |
process error |
oe |
process error |
bk |
initial fraction of b/k |
years |
time horizon |
f0 |
factor for initial year as f0 = f/fmsy |
fhi |
factor for high F as fhi = f/fmsy |
flo |
factor for low F as flo = fbar/fmsy |
sigmaF |
variation on f trajectory |
iters |
number of iterations |
rel |
if TRUE metrics B/Bmsy and F/Fmsy are produced |
FLQuants
stk = schaefer.sim(iters=100,q=0.5) plotAdvice(stk) plot(FLIndex(index=iter(stk@stock,1))) # indexstk = schaefer.sim(iters=100,q=0.5) plotAdvice(stk) plot(FLIndex(index=iter(stk@stock,1))) # index
scales catch-at-age to total catch with error (optional)
sops(object, stock, sigma = 0.1, what = c("catch", "landings", "discards")[1])sops(object, stock, sigma = 0.1, what = c("catch", "landings", "discards")[1])
object |
FLQuant catch.n, discard.n, landings.n |
stock |
FLStock |
sigma |
observation error |
what |
type c("catch", "landings", "discards") |
FLQuant
spict2FLQuant()
spict2FLQuant( x, metric = c("ssb", "fbar", "catch", "stock", "harvest")[1], osa = FALSE, forecast = F )spict2FLQuant( x, metric = c("ssb", "fbar", "catch", "stock", "harvest")[1], osa = FALSE, forecast = F )
x |
fit from SPICT |
osa |
add one-step-ahead forecast |
forecast |
TRUE/FALSE |
FLQuant
adopted from Laurie Kell (biodyn)
spict2FLStockR()
spict2FLStockR( res, blim = 0.3, bthr = 0.5, rel = FALSE, osa = FALSE, forecast = NULL )spict2FLStockR( res, blim = 0.3, bthr = 0.5, rel = FALSE, osa = FALSE, forecast = NULL )
res |
fit from SPICT |
blim |
biomass limit reference point as fraction of Bmsy |
bthr |
biomass precautionary reference point as fraction of Bmsy |
rel |
if TRUE ratios BBmsy and FFmsy are stored |
osa |
add one-step-ahead forecast |
forecast |
extract forecast TRUE/FALSE |
FLStockR with refpts
spict2stars()
spict2stars(spict, blim = 0.3, bthr = 0.5)spict2stars(spict, blim = 0.3, bthr = 0.5)
spict |
fit from fit.spict() |
blim |
biomass limit point as fraction of Bmsy, default 0.3Bmsy (ICES) |
bthr |
biomass precautionary point as fraction of Bmsy, default 0.5Bmsy (ICES) |
STARS list with $timeseris and $refpts
ss2FLStockR()
ss2FLStockR(mvln, thin = 10, output = NULL)ss2FLStockR(mvln, thin = 10, output = NULL)
mvln |
output from ssmvln() |
thin |
thinnig rate of retained iters |
output |
expected outputs presented as "mle" or median of "iters" |
FLStockR with refpts
ss2stars()
ss2stars(mvln, output = c("iters", "mle")[1], quantiles = c(0.025, 0.975))ss2stars(mvln, output = c("iters", "mle")[1], quantiles = c(0.025, 0.975))
mvln |
output of ssmvln() |
output |
choice c("iters","mle")[1] |
quantiles |
default is 95CIs as c(0.025,0.975) |
STARS list with $timeseris and $refpts
ss3col
ss3col(n, alpha = 1)ss3col(n, alpha = 1)
n |
number of colors |
alpha |
transluscency |
vector of color codes
function to generate uncertainty for Stock Synthesis
ssmvln( ss3rep, Fref = NULL, years = NULL, virgin = FALSE, mc = 1000, weight = 1, run = "MVLN", addprj = FALSE, ymax = NULL, xmax = NULL, legendcex = 1, verbose = TRUE, seed = 123 )ssmvln( ss3rep, Fref = NULL, years = NULL, virgin = FALSE, mc = 1000, weight = 1, run = "MVLN", addprj = FALSE, ymax = NULL, xmax = NULL, legendcex = 1, verbose = TRUE, seed = 123 )
ss3rep |
from r4ss::SS_output |
Fref |
Choice of Fratio c("MSY","Btgt","SPR","F01"), correponding to F_MSY and F_Btgt |
years |
single year or vector of years for mvln |
virgin |
if FALSE (default) the B0 base for Bratio is SSB_unfished |
mc |
number of monte-carlo simulations |
weight |
weighting option for model ensembles weight*mc |
run |
qualifier for model run |
addprj |
include forecast years |
ymax |
ylim maximum |
xmax |
xlim maximum |
verbose |
Report progress to R GUI? |
seed |
retains interannual correlation structure like MCMC |
out |
choice c("iters","mle") |
plot |
option to show plot |
legendcex=1 |
Allows to adjust legend cex |
output list of quant posteriors and mle's
Henning Winker (GFCM)
stock2ratios()
stock2ratios(object)stock2ratios(object)
object |
of class *FLStockR* |
FLStockR with ratios F/Ftgt and B/Btgt
stockMedians
stockMedians(stock)stockMedians(stock)
stock |
class FLStock or FLStockR |
medians of all FLstock FlQuants
updates sr in brp after changing biology
updsr(object, s = 0.7, v = NULL)updsr(object, s = 0.7, v = NULL)
object |
An *FLBRP* |
s |
assumed steepness s |
v |
input option new SB0 |
FLBRP
data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) s = sr@SV[[1]] params(brp) # change m(brp) = Mlorenzen(stock.wt(brp),Mref=0.15) brpupd =updsr(brp,s) params(brp)data(ple4) sr <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4))) brp = FLBRP(ple4,sr) s = sr@SV[[1]] params(brp) # change m(brp) = Mlorenzen(stock.wt(brp),Mref=0.15) brpupd =updsr(brp,s) params(brp)
updstars()
updstars(star, newrefpts)updstars(star, newrefpts)
star |
output of star list |
newrefpts |
manually adjusted reference points |
STARS list with $timeseris and $refpts