Package 'FLSRTMB'

Title: FLSR in TMB
Description: Estimates FLR spawner recruitment relationships in TMB
Authors: Henning Winker [aut, cre], European Union [fnd, cph], Iago Mosqueira [aut] , Christoffer Moesgaard Albertsen [ctb]
Maintainer: Henning Winker <[email protected]>
License: EUPL
Version: 1.1.4.9015
Built: 2024-11-26 11:20:45 UTC
Source: https://github.com/flr/FLSRTMB

Help Index

blimprior plot Plots the bounds of the hockey-stick break-point

Description

blimprior plot Plots the bounds of the hockey-stick break-point

Usage

blimprior(
  lplim = 0.01,
  uplim = 0.3,
  s.logitsd = 50,
  par = "plim",
  bias.correct = TRUE
)

Arguments

lplim

steepness, default 0.6 for a approx. uniform prior with s.logistsd = 20
uplim

s steepness, default 20 for a approx. uniform prior with s = 0.6
par

parameter on x-axis default "plim", else "sstar"
s.sdlogit

default 20

Value

ggplot

Examples

blimprior() # approx. uniform with some curving on bounds
blimprior(lplim=0.001,uplim=0.3,s.logitsd=20) 
# Non-bias corrected
blimprior(lplim=0.001,uplim=0.3,s.logitsd=20,bias.correct=FALSE)

Bootstrap fits of mutliple stock-recruits relationships

Description

Definition ...

Usage

bootstrapSR(
  x,
  iters = 100,
  method = c("best", "logLik", "relative"),
  models = c("bevholt", "ricker", "segreg"),
  verbose = TRUE,
  ...
)

Arguments

x

An object of class 'FLStock'.
models

Name(s) of model(s) to fit, 'character'. See Details.
verbose

Should progress be reported, 'logical'.
iter

Number of bootstrap iterations, 'numeric'.

Details

The returned 'FLPar' object contains

Value

A list with elements An object or class 'FLPar' containing the estimated paramaters.

Author(s)

Iago Mosqueira (WMR)

See Also

FLSR, linksrrTMB

Examples

data(ple4)
mods <- bootstrapSR(ple4, iter=50, model=c("bevholt", "segreg"))

dprior plot Plots the logit prior distribution for depensation

Description

dprior plot Plots the logit prior distribution for depensation

Usage

dprior(d = 1, d.logitsd = 100, ll = 0.5, ul = 3)

Arguments

d

depensation, default 1 for a approx. uniform prior with s.logistsd = 20
d.logitsd

dependation sd, default 20 for a approx. uniform prior with s = 0.6
ll

lower bound of d = 0.25
ul

lower bound of d = 4

Value

ggplot

Examples

dprior() # approx. uniform with some curving on bounds
dprior(d=1,d.logitsd=2)

from_logitd()

Description

convert depensation d from logit

Usage

from_logitd(logit_d, ll = 0.5, ul = 3)

Arguments

logit_d

logit(depensation)
ll

defines lower prior bound of d
ul

defines upper prior bound of d

Value

depensation d

from_logits()

Description

convert steepness from logit

Usage

from_logits(logit_s, ll = 0.2, ul = 1)

Arguments

logit_s

logit(steepness)
ll

defines lower prior bound of s
ul

defines upper prior bound of s

Value

steepness s

gm()

Description

Generic geometric mean function

Usage

gm(x)

Arguments

x

object

Value

FLQuant with annual sprFy

hsblim()

Description

helper to quickly extract blim, r0 and srp0 = blim/b0 from hockeystick

Usage

hsblim(object)

Arguments

object

class FLSR fitted with model segreg

Value

FLPar

plots Jitter results

Description

plots Jitter results

Usage

plotjitter(jitter)

Arguments

jitter

output from srrjitter()

Value

ggplot

Examples

data(ple4)
hs = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4),lplim=0.07,uplim=0.2)
plotsrs(hs)
jitter = srrjitter(hs)
plotsrs(jitter$groups)
plotjitter(jitter)
bh = srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4))
jitbh = srrjitter(bh)
plotjitter(jitbh)
plotsrs(jitbh$groups)

plotsrs based on plot(FLSRs) Plots FLSRs, i.e. multiple S-R relationships

Description

plotsrs based on plot(FLSRs) Plots FLSRs, i.e. multiple S-R relationships

Usage

plotsrs(object, path = TRUE, b0 = FALSE, rel = FALSE)

Arguments

object

of class FLSRS
path

connect points sequentially

Value

ggpplot

Examples

data(ple4)
bh <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4)))
plotsrs(bh)
plotsrs(bh,b0=TRUE) # plot through to B0
plotsrs(bh,b0=TRUE,rel=TRUE) # plot relative to B0
# Try more models
hs <- srrTMB(as.FLSR(ple4,model=segreg),spr0=(spr0y(ple4)),plim=0.05,pmax=0.2)
ri <- srrTMB(as.FLSR(ple4,model=rickerSV),spr0=mean(spr0y(ple4)))
bh.tv <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4))
ri.tv <- srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4))
all= FLSRs(hs=hs,bh=bh,ri=ri,bh.tv=bh.tv,ri.tv=ri.tv)
plotsrs(all)
plotsrs(all,path=FALSE)
# plot through to b0
plotsrs(all,b0=TRUE)
# plot all relative relative to B0
plotsrs(all,rel=TRUE)
plotsrs(all,rel=TRUE,b0=TRUE)

Plots time series of observed and predicted recruitment

Description

Plots time series of observed and predicted recruitment

Usage

plotsrts(object, relative = TRUE)

Arguments

object

Input FLSR of FLSRs object

Value

ggplot

Examples

data(ple4)
bh <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=mean(spr0y(ple4)))
plotsrts(bh)
# Try more models
hs <- srrTMB(as.FLSR(ple4,model=segreg),spr0=(spr0y(ple4)),plim=0.05,pmax=0.2)
ri <- srrTMB(as.FLSR(ple4,model=rickerSV),spr0=mean(spr0y(ple4)))
bh.tv <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4))
ri.tv <- srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4))
all= FLSRs(hs=hs,bh=bh,ri=ri,bh.tv=bh.tv,ri.tv=ri.tv)
plotsrts(all)
do.call(c,lapply(all,function(x)AIC(x))) # AIC

productivity()

Description

Function to compute r, generation time (gt) and annual reproductive rate (alpha)

Usage

productivity(object, s = 0.7)

Arguments

object

class FLStock
s

steepness of the stock recruitment relationship

Value

FLQuants with FLQuant r, gt and alpha

Author(s)

Henning Winker and Laurence Kell

spr0y()

Description

Function to compute annual spr0

Usage

spr0y(object, byage = FALSE, simplify = TRUE)

Arguments

object

class FLStock
byage

if TRUE it return spr0_at_age

Value

FLQuant with annual spr0y

Author(s)

Laurence Kell

sprFy()

Description

Function to compute annual sprF as function of F_a

Usage

sprFy(object, byage = FALSE)

Arguments

object

class FLStock
byage

if TRUE it return sprF_at_age

Value

FLQuant with annual sprFy

Author(s)

Henning Winker and Laurence Kell

sprior plot Plots the logit prior distribution for steepness

Description

sprior plot Plots the logit prior distribution for steepness

Usage

sprior(s = 0.6, s.logitsd = 20, ll = 0.2, ul = 1)

Arguments

s

steepness, default 0.6 for a approx. uniform prior with s.logistsd = 20
s.logitsd

s steepness, default 20 for a approx. uniform prior with s = 0.6
ll

lower bound of s = 0.2
ul

lower bound of s = 1

Value

ggplot

Examples

sprior() # approx. uniform with some curving on bounds
sprior(s=0.8,s.logitsd=0.5)

Jitter of S-R fits

Description

Jitter of S-R fits

Usage

srrjitter(fit, steps = 100)

Arguments

fit

fit of srrTBM()
steps

number of jitter steps

Value

list

Examples

data(ple4)
hs = srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4),lplim=0.07,uplim=0.5)
plotsrs(hs)
jitter = srrjitter(hs)
plotsrs(jitter$groups)
plotsrs(jitter$best) # Best
# Relax lower bound
plotsrs(FLSRs(init=hs,best=jitter$best))

Fits Stock Recruitment Relationships (SRR) in TMB

Description

Fits Stock Recruitment Relationships (SRR) in TMB

Usage

srrTMB(object, ...)

## S4 method for signature 'FLSRs'
srrTMB(object, ...)

## S4 method for signature 'FLSR'
srrTMB(
  object,
  spr0 = "missing",
  s = NULL,
  s.est = TRUE,
  s.logitsd = 50,
  r0.pr = "missing",
  lplim = 0.01,
  uplim = 0.3,
  Blim = "missing",
  d = 1,
  d.est = TRUE,
  d.logitsd = 100,
  ld = 0.5,
  ud = 3,
  plim = lplim,
  pmax = uplim,
  nyears = NULL,
  report.sR0 = FALSE,
  inits = NULL,
  lower = NULL,
  upper = NULL,
  SDreport = TRUE,
  verbose = FALSE,
  rm.yrs = "missing",
  bias.correct = TRUE
)

Arguments

object

Input FLSR = as.FLSR(stock,model) object with current model options

  • bevholtSV

  • bevholtDa

  • rickerSV

  • segreg

  • geomean

spr0

unfished spawning biomass per recruit from FLCore::spr0(FLStock)
s

steepness parameter of SRR (fixed or prior mean)
s.est

option to estimate steepness
s.logitsd

prior sd for logit(s), default is 1.4 (flat) if s.est = TRUE
r0.pr

option to condition models on r0 priors (NULL = geomean)
lplim

lower bound of spawning ratio potential SRP, default 0.0001
uplim

upper bound of plausible spawning ratio potential SRP , default 0.3
Blim

fixing Blim, only works with segreg
d

depensation parameter (default = 1)
d.est

option to estimate depensation d
d.logitsd

priod sd for logit(d)
ld

lower bound of depensation parameter d
ud

upper bound of depensation parameter d
plim

depreciated plim = usrp
pmax

depreciated pmax = lsrp
nyears

yearMeans from the tail used to compute a,b from the reference spr0 (default all years)
report.sR0

option to report s and R0 instead of a,b
inits

option to specify initial values of log(r0), log(SigR) and logit(s)
lower

option to specify lower bounds of log(r0), log(SigR) and logit(s)
upper

option to specify upper bounds of log(r0), log(SigR) and logit(s)
SDreport

option to converge hessian and get vcov
verbose

if TRUE, it shows tracing
rm.yrs

remove recruitment years from model fit
bias.correct

if TRUE, bias correction of the uniform logistic hockey-stick prior for Blim/B0 (recommended)

Value

A list containing elements 'FLSR', of class FLSR

Examples

data(ple4)
gm <- srrTMB(as.FLSR(ple4,model=geomean),spr0=mean(spr0y(ple4)))
bh <- srrTMB(as.FLSR(ple4,model=bevholtSV),spr0=spr0y(ple4))
ri <- srrTMB(as.FLSR(ple4,model=rickerSV),spr0=spr0y(ple4))
hs <- srrTMB(as.FLSR(ple4,model=segreg),spr0=spr0y(ple4),lplim=0.05,uplim=0.2)
srs = FLSRs(gm=gm,bh=bh,ri=ri,hs=hs) # combine
plotsrs(srs) 
plotsrts(srs)  # relative
plotsrs(srs[2:4],b0=TRUE) # through to B0
plotsrs(srs[2:4],b0=TRUE,rel=TRUE)  # relative
gm@SV # estimates
do.call(rbind,lapply(srs,AIC))
# Bias-correction test for Hockey-Stick
srs=FLSRs(
 correct = srrTMB(as.FLSR(ple4,model=segreg),spr0=mean(spr0y(ple4)),lplim=0.001,uplim=0.08),
 bias =srrTMB(as.FLSR(ple4,model=segreg),spr0=mean(spr0y(ple4)),lplim=0.001,uplim=0.08,bias.correct=F,s.logitsd=20))
plotsrs(srs,rel=T)
# Depensation
d.srs = FLSRs(
uniform = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4)),
larger1 = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4),ld=1),
prior1 = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4),d=1.5,d.logitsd=1.5),
prior1.5 = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4),d=1.5,d.logitsd=1.5),
fixed1.5 = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4),d=1.5,d.est=FALSE),
fixed2.5 = srrTMB(as.FLSR(ple4,model=bevholtDa),spr0=spr0y(ple4),d=2.5,d.est=FALSE)
)
plotsrs(d.srs)

to_logitd()

Description

convert steepness to logit

Usage

to_logitd(d, ll = 0.5, ul = 3)

Arguments

d

steepness s
ll

defines lower prior bound of s
ul

defines upper prior bound of s

Value

logit transformed steepness s

to_logits()

Description

convert steepness to logit

Usage

to_logits(s, ll = 0.2, ul = 1)

Arguments

s

steepness s
lim

defines lower prior bound of s
max

defines upper prior bound of s

Value

logit transformed steepness s