Package 'FLa4a'

Description

Update FLStock and FLIndex objects with simulations from stock assessment fits.

Usage

## S4 method for signature 'FLStock,a4aFitSA'
e1 * e2

## S4 method for signature 'FLStock,SCAPars'
e1 * e2

## S4 method for signature 'FLIndices,a4aFitSA'
e1 * e2

## S4 method for signature 'FLIndices,SCAPars'
e1 * e2

Arguments

Description

The a4aFit class was built to store the a4a stock assessment fits.

Usage

a4aFit(...)

a4aFit(...)

clock(object, ...)

## S4 method for signature 'a4aFit'
clock(object)

fitSumm(object, ...)

## S4 method for signature 'a4aFit'
fitSumm(object)

## S4 method for signature 'a4aFit'
stock.n(object)

## S4 method for signature 'a4aFit,ANY'
harvest(object)

## S4 method for signature 'a4aFit'
catch.n(object)

## S4 method for signature 'a4aFit'
index(object)

## S4 method for signature 'a4aFit'
show(object)

## S4 method for signature 'a4aFit'
logLik(object, ...)

## S4 method for signature 'a4aFit'
iter(obj, it)

## S4 method for signature 'a4aFit'
computeCatchDiagnostics(object, stock, ...)

Arguments

Slots

call

The function call

clock

Information on call duration

fitSumm

Fit summary

stock.n

Estimates of stock numbers-at-age

harvest

Estimates of fishing mortality at age

catch.n

Estimates of catch numbers-at-age

index

Estimates of survey or CPUE indices-at-age

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Examples

data(ple4)
data(ple4.index)

obj <- sca(stock=ple4, indices=FLIndices(ple4.index))
obj

slotNames(obj)
clock(obj)
fitSumm(obj)

flq <- stock.n(obj)
is(flq)
flq <- index(obj)
is(flq)

logLik(obj)
AIC(obj)
BIC(obj)

Description

The a4aFitCatchDiagn class extends FLQuants to store information to run diagnostics on aggregated catch estimated by the a4a stock assessment fit.

Usage

computeCatchDiagnostics(object, ...)

Arguments

Examples

data(ple4)
data(ple4.index)
obj <- sca(stock=ple4, indices=FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))

Description

The a4aFitMCMC class extends a4aFitSA to store information about the MCMC run.

Usage

a4aFitMCMC(...)

a4aFitMCMC(...)

## S4 method for signature 'a4aFitMCMC'
a4aFitSA(object, ...)

## S4 method for signature 'a4aFitMCMC'
a4aFit(object, ...)

as.mcmc(x, ...)

## S4 method for signature 'a4aFitMCMC'
as.mcmc(x, ...)

burnin(object, ...)

## S4 method for signature 'a4aFitMCMC'
burnin(object, burnin)

Arguments

Slots

name

A character vector for the object name.

desc

A textual description of the object contents.

range

A named numeric vector with various values of quant and year ranges, plusgroup, fishing mortality ranges, etc.

call

The function call

clock

Information on call duration

fitSumm

Fit summary

stock.n

Estimates of stock numbers-at-age

harvest

Estimates of fishing mortality at age

catch.n

Estimates of catch numbers-at-age

index

Estimates of survey or CPUE indices-at-age

mcmc

An object of class SCAMCMC with information about the MCMC run

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Examples

data(ple4)
data(ple4.index)

obj <- sca(stock=ple4, indices=FLIndices(ple4.index), fit="assessment")
obj

slotNames(obj)
clock(obj)
fitSumm(obj)

flq <- stock.n(obj)
is(flq)
flq <- index(obj)
is(flq)

logLik(obj)
AIC(obj)
BIC(obj)

is(pars(obj))

Description

The a4aFitResiduals class extends FLQuants to store residuals of the a4a stock assessment fit. By default, these should be log residuals of catches and indices.

Usage

## S4 method for signature 'a4aFit'
residuals(object, stock, indices, ...)

Arguments

Examples

data(ple4)
data(ple4.index)
obj <- sca(stock=ple4, indices=FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))

Description

The a4aFitSA class extends a4aFit to store information about the parameters of the model.

Usage

a4aFitSA(...)

a4aFitSA(...)

## S4 method for signature 'a4aFitSA'
a4aFit(object, ...)

pars(object)

## S4 method for signature 'a4aFitSA'
pars(object)

## S4 method for signature 'a4aFitSA'
m(object)

## S4 method for signature 'a4aFitSA'
wt(object)

## S4 method for signature 'a4aFitSA'
qmodel(object)

## S4 method for signature 'a4aFitSA'
fmodel(object)

## S4 method for signature 'a4aFitSA'
srmodel(object)

## S4 method for signature 'a4aFitSA'
n1model(object)

## S4 method for signature 'a4aFitSA'
vmodel(object)

## S4 method for signature 'a4aFitSA'
stkmodel(object)

## S4 method for signature 'a4aFitSA'
show(object)

## S4 method for signature 'a4aFitSA'
submodels(object, ...)

## S4 method for signature 'a4aFitSA'
iter(obj, it)

a4aFitSAs(object, ...)

## S4 method for signature 'list'
a4aFitSAs(object, ...)

## S4 method for signature 'a4aFitSA'
a4aFitSAs(object, ...)

## S4 method for signature 'missing'
a4aFitSAs(object, ...)

Arguments

Slots

call

The function call

clock

Information on call duration

fitSumm

Fit summary

stock.n

Estimates of stock numbers-at-age

harvest

Estimates of fishing mortality at age

catch.n

Estimates of catch numbers-at-age

index

Estimates of survey or CPUE indices-at-age

pars

an object of class SCAPars with information about model parameters

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Examples

data(ple4)
data(ple4.index)

obj <- sca(stock=ple4, indices=FLIndices(ple4.index), fit="assessment")
obj

slotNames(obj)
clock(obj)
fitSumm(obj)

flq <- stock.n(obj)
is(flq)
flq <- index(obj)
is(flq)

logLik(obj)
AIC(obj)
BIC(obj)

is(pars(obj))

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

a4aGr(object, ...)

## S4 method for signature 'missing'
a4aGr(object, ...)

grMod(object, ...)

## S4 method for signature 'a4aGr'
grMod(object)

grMod(object) <- value

## S4 replacement method for signature 'a4aGr,formula'
grMod(object) <- value

grInvMod(object, ...)

## S4 method for signature 'a4aGr'
grInvMod(object)

grInvMod(object) <- value

## S4 replacement method for signature 'a4aGr,formula'
grInvMod(object) <- value

## S4 method for signature 'a4aGr'
params(object)

## S4 replacement method for signature 'a4aGr,FLPar'
params(object) <- value

## S4 method for signature 'a4aGr'
distr(object)

## S4 replacement method for signature 'a4aGr,character'
distr(object) <- value

## S4 method for signature 'a4aGr'
vcov(object)

## S4 replacement method for signature 'a4aGr,numeric'
vcov(object) <- value

Arguments

Slot

grMod

the formula for the growth model, e.g. von Bertallanffy

grInvMod

the formula for the inverse of the growth model, having length as the independent variable

params

an FLPar object with the parameters of the model; must match the equations in the models

vcov

an array with the variance covariance matrix of the parameters

distr

a character with the parameters' statistical distribution; it must match a known distribution for R (e.g. "norm" for gaussian), so that rnorm can be called

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Examples

mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")

Description

The advanced user interface to the a4a fitting routine.

Usage

a4aInternal(
  stock,
  indices,
  fmodel = defaultFmod(stock),
  qmodel = defaultQmod(indices),
  srmodel = defaultSRmod(stock),
  n1model = defaultN1mod(stock),
  vmodel = defaultVmod(stock, indices),
  covar = missing,
  wkdir = missing,
  verbose = FALSE,
  fit = "assessment",
  center = TRUE,
  mcmc = missing
)

Arguments

Value

an a4aFit object if fit is "MP" or an a4aFitSA if fit is "assessment"

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

a4aM(object, ...)

## S4 method for signature 'missing'
a4aM(object, ...)

## S4 method for signature 'a4aM'
show(object)

shape(object, ...)

## S4 method for signature 'a4aM'
shape(object)

shape(object) <- value

## S4 replacement method for signature 'a4aM'
shape(object) <- value

level(object, ...)

## S4 method for signature 'a4aM'
level(object)

level(object) <- value

## S4 replacement method for signature 'a4aM'
level(object) <- value

trend(object, ...)

## S4 method for signature 'a4aM'
trend(object)

trend(object) <- value

## S4 replacement method for signature 'a4aM'
trend(object) <- value

Arguments

Slot

shape

the shape of M by age

level

the mean level of M over a range of ages, which will be used to scale the shape

trend

the yearly trend in M

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Examples

mod1 <- FLModelSim(model=~exp(-age-0.5))
mod2 <- FLModelSim(model=~1.5*k, params=FLPar(k=0.4))
m1 <- a4aM(shape=mod1, level=mod2)

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

a4aStkParams(object, ...)

## S4 method for signature 'missing'
a4aStkParams(object, ...)

## S4 method for signature 'a4aStkParams'
m(object)

## S4 method for signature 'a4aStkParams'
wt(object)

## S4 method for signature 'a4aStkParams'
mat(object)

fMod(object, ...)

## S4 method for signature 'a4aStkParams'
fMod(object)

fMod(object) <- value

## S4 replacement method for signature 'a4aStkParams,formula'
fMod(object) <- value

n1Mod(object, ...)

## S4 method for signature 'a4aStkParams'
n1Mod(object)

n1Mod(object) <- value

## S4 replacement method for signature 'a4aStkParams,formula'
n1Mod(object) <- value

srMod(object, ...)

## S4 method for signature 'a4aStkParams'
srMod(object)

srMod(object) <- value

## S4 replacement method for signature 'a4aStkParams,formula'
srMod(object) <- value

## S4 method for signature 'a4aStkParams'
params(object)

## S4 replacement method for signature 'a4aStkParams,FLPar'
params(object) <- value

coefficients(object, ...)

## S4 method for signature 'a4aStkParams'
coefficients(object)

coefficients(object) <- value

## S4 replacement method for signature 'a4aStkParams,FLPar'
coefficients(object) <- value

## S4 method for signature 'a4aStkParams'
distr(object)

## S4 replacement method for signature 'a4aStkParams,character'
distr(object) <- value

## S4 method for signature 'a4aStkParams'
vcov(object)

## S4 replacement method for signature 'a4aStkParams,array'
vcov(object) <- value

## S4 method for signature 'a4aStkParams'
propagate(object, iter, fill.iter = TRUE)

## S4 method for signature 'a4aStkParams'
iter(obj, it)

Arguments

Slot

fMod

F submodel formula

n1Mod

first year N formula

srMod

stock-recruitment submodel formula

params

FLPar with parameters

vcov

array with variance-covariance

centering

centering values numeric

distr

statistical distribution character

m

natural mortality FLQuant

units

data units character

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Description

Update FLStock and FLIndex objects with stock assessment results.

Usage

## S4 method for signature 'FLStock,a4aFit'
e1 + e2

## S4 method for signature 'FLIndices,a4aFit'
e1 + e2

Arguments

Details

If both objects have the same number of iterations, the FLStock slots will be replaced by the a4aFit slots, in the case of 1 iter, or a4aFitSA slots, in the case of n iters. If one of the objects has 1 iter and the other n, the method will simulate using the fit results from the a4aFitSA object to update the slots of the FLStock object.

Description

Assorted methods needed by FLa4a

Assorted methods needed by FLa4a

Usage

getYidx(object, ...)

## S4 method for signature 'FLQuant'
getYidx(object, year)

is.empty(object)

niters(object, ...)

## S4 method for signature 'FLModelSim'
niters(object)

## S4 method for signature 'a4aGr'
niters(object)

## S4 method for signature 'a4aStkParams'
dims(obj)

replaceZeros(object, ...)

## S4 method for signature 'FLQuant'
replaceZeros(object, fraction = 0.25)

## S4 method for signature 'FLStock'
replaceZeros(object, fraction = 0.25)

## S4 method for signature 'FLIndex'
replaceZeros(object, fraction = 0.25)

## S4 method for signature 'FLIndices'
replaceZeros(object, fraction = 0.25)

Arguments

getYidx

Gets an FLQuant's numeric id for a vector of "years". For internal use and not very interesting for users. It takes an FLQuant object and vector of years and returns a numeric vector that can be used to subset the FLQuant.

is.empty

Method is.empty checks if an object is empty. It takes any object and returns a logical, TRUE, if the object is of length 0.

niters

Compute number of iterations. Takes an object of any FLR class and returns a numeric.

dims

Extracts the dims of the parameters.

replaceZeros

Replaces observations of 0 by a fraction of the minimum observed. It takes an FLQuant object and numeric of min fraction and returns a FLQuant with zeros replaced to be added to the FLStock or FLIndex objects.

Examples

#Example use of getYidx:
data(ple4)
flq <- catch(ple4)
getYidx(flq, 2000:2004)
flq[, getYidx(flq, 2000:2004)]
#Example use of is.empty:
is.empty(list())
is.empty(list(a=2))
#Example use of niters:
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
# Generate 100 sample sets
vbObj <- mvrnorm(100,vbObj)
niters(vbObj)
#Example use of dims:
dims(FLPar())
#Example use of getYidx:
data(ple4)
flq <- catch(ple4)
flq <- replaceZeros(flq, 0.25)
catch(ple4) <- flq

Description

Method to set breakpoints in submodels

Usage

breakpts(var, ...)

## S4 method for signature 'numeric'
breakpts(var, breaks, ...)

Arguments

Value

a factor with levels according to the defined breaks

Description

Method to produce bubble plots of standardized residuals

Usage

## S4 method for signature 'a4aFitResiduals,missing'
bubbles(x, data = missing, ...)

Arguments

Value

a bubbles plot with stardardized log residuals

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))
bubbles(flqs)

Description

Method to collapse seasons of FLStock objects. M and catch-at-age are summed while mean weights at age, maturity at age and mortalities before spawning are averaged.

Usage

collapseSeasons(stock)

Arguments

Value

a FLStock object

Description

Methods to create formulas for sub-models. The sub-models are set automagically using defaults.

Usage

defaultFmod(stock, dfm = c(0.5, 0.7))

defaultQmod(indices, dfm = 0.6)

defaultN1mod(stock)

defaultVmod(stock, indices)

defaultSRmod(stock)

Arguments

Value

a FLStock object

Description

Deprecated methods.

Usage

a4aSCA(...)

Arguments

Description

Methods to extract and replace the model coefficients.

Usage

formula(object) <- value

## S4 replacement method for signature 'submodel,formula'
formula(object) <- value

coef(object, ...)

## S4 method for signature 'a4aFitSA'
coef(object)

## S4 method for signature 'SCAPars'
coef(object)

## S4 method for signature 'a4aStkParams'
coef(object)

## S4 method for signature 'submodels'
coef(object)

## S4 method for signature 'submodel'
coef(object)

coef(object, ...) <- value

## S4 replacement method for signature 'a4aFitSA,numeric'
coef(object, ...) <- value

## S4 replacement method for signature 'SCAPars,numeric'
coef(object, ...) <- value

## S4 replacement method for signature 'a4aStkParams,numeric'
coef(object, ...) <- value

## S4 replacement method for signature 'submodels,numeric'
coef(object, ...) <- value

## S4 replacement method for signature 'submodel,numeric'
coef(object, ...) <- value

## S4 replacement method for signature 'submodel,FLPar'
coef(object, ...) <- value

## S4 replacement method for signature 'a4aStkParams,FLPar'
coef(object, ...) <- value

## S4 replacement method for signature 'a4aStkParams,matrix'
coef(object, ...) <- value

Arguments

Description

This method produces an FLIndex object by using the genFLQuant method.

Usage

genFLIndex(object, ...)

## S4 method for signature 'FLQuant'
genFLIndex(object, cv = 0.2, niter = 250)

Arguments

Value

an FLIndex

Description

This method uses the quant log-correlation matrix of the FLQuant object and generates a new FLQuant using a lognormal multivariate distribution.

Usage

genFLQuant(object, ...)

## S4 method for signature 'FLQuant'
genFLQuant(object, cv = 0.2, method = "ac", niter = 250)

## S4 method for signature 'submodel'
genFLQuant(object, type = c("link", "response"), nsim = 0, seed = NULL)

## S4 method for signature 'submodels'
genFLQuant(object, type = c("link", "response"), nsim = 0, seed = NULL)

## S4 method for signature 'a4aStkParams'
genFLQuant(
  object,
  type = c("link", "response"),
  nsim = 0,
  seed = NULL,
  simulate.recruitment = FALSE
)

Arguments

Value

an FLQuant

Examples

data(ple4)
sim.F <- genFLQuant(harvest(ple4))

Description

This method computes the FLStock slots consistently with the information provided by the FLQuant. It requires two of the triplet R/C/F to compute the third consistent with Baranov and survival's equations.

Usage

genFLStock(object, R, C, F, ...)

## S4 method for signature 'FLStock,FLQuant,FLQuant,missing'
genFLStock(object, R, C, F, ...)

## S4 method for signature 'FLStock,missing,FLQuant,FLQuant'
genFLStock(object, R, C, F, ...)

## S4 method for signature 'FLStock,FLQuant,missing,FLQuant'
genFLStock(object, R, C, F, ...)

Arguments

Value

an FLStock

Description

Method to compute the log-correlation matrix for the first dimension (quant) of the FLQuant object.

Usage

getAcor(object, ...)

## S4 method for signature 'FLQuant'
getAcor(object, ...)

Arguments

Value

an FLQuant object with a quant log-correlation matrix

Examples

data(ple4)
getAcor(harvest(ple4))

Description

Reads the hessian file from any ADMB fit. Used here with the a4a model.

Usage

getADMBHessian(wkdir)

getADMBCovariance(wkdir)

Arguments

Value

a list with the following elements

Note

getADMBHessian is intended to be used internally

Examples

# load some data
data(ple4)
data(ple4.indices)
# choose a working directory
wkdir <- tempfile()
# do an 'assessment' fit with default settings (not recomended!) and keep results in wkdir
fit <- sca(stock=ple4,indices=ple4.indices,wkdir=wkdir)
hessInfo <- getADMBHessian(wkdir)
str(hessInfo)
# calculate covariance matrix
Sigma <- solve(hessInfo$hes)

Description

Returns the covariance matrix of the specified Gaussian markov random field model.

Usage

getCov(n, model, tau)

Arguments

Value

a covariance matrix

Description

Method to get values of the growth parameter K

Usage

getK(object, ...)

## S4 method for signature 'a4aGr'
getK(object)

Arguments

Value

a vector with K values

Examples

mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
vbObj <- mvrnorm(100,vbObj)
getK(vbObj)

Description

Function to get the a4a TPL file with ADMB code and copy into a specific folder.

Usage

getTPL(dir)

Arguments

Value

file a4a.tpl

Examples

getTPL("myfolder")

Description

Uses the user-specified formula to build a model matrix.

Usage

getX(object, ...)

## S4 method for signature 'formula'
getX(object, df, newdf = df)

Arguments

Value

a matrix.

Note

getX is intended to be used internally

Description

Catch number, stocks weights, etc. for Gulf of Lions Hake (1998 - 2011).

Usage

hakeGSA7

Format

an FLStock object

Author(s)

Chato Osio

Source

GFCM - STECF

Description

Survey index for the Gulf of Lions Hake stock.

Usage

hakeGSA7.idx

Format

an FLIndices object

Author(s)

Chato Osio

Source

GFCM - STECF - MEDITS

Description

Survey abundance index for hake in the Gulf of Cadiz. Lenth frequencies.

Usage

index_cd_len

Format

an FLIndex object

Author(s)

Santiago Cervi\~no

Description

Survey abundance index for hake in Portuguese continental waters. Lenth frequencies.

Usage

index_pt_len

Format

an FLIndex object

Author(s)

Santiago Cervi\~no

Description

Survey abundance index for hake in Northwest Spanish waters. Lenth frequencies.

Usage

index_sp_len

Format

an FLIndex object

Author(s)

Santiago Cervi\~no

Description

Method to convert length-based data to age-based

Usage

l2a(object, model, ...)

## S4 method for signature 'FLQuant,a4aGr'
l2a(
  object,
  model,
  halfwidth = c(diff(as.numeric(dimnames(object)[[1]])),
    tail(diff(as.numeric(dimnames(object)[[1]])), 1))/2,
  stat = "sum",
  max_age = NA
)

## S4 method for signature 'FLStockLen,a4aGr'
l2a(object, model, plusgroup = NA, ...)

## S4 method for signature 'FLIndex,a4aGr'
l2a(object, model, ...)

Arguments

Details

A deterministic slicing method converts the length-based data to age-based data, using the supplied growth model (the a4aGr object). Each length-based observation is allocated to a corresponding age, based on the growth model, and aggregated accordingly (either the sum or the mean). There should be 1 or n iterations in both the object being sliced and the growth model. This means that although the slicing method is deterministic, the length-based data is sliced by each iteration of the growth parameters, thereby propagating the uncertainty in the biological growth parameters (representing process uncertainty) through to the age-based data.

Value

an age based FLQuant, FLStock

Examples

# Southern hake
# Variance-covariance matrix for parameters
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(2310, 0.13, 0.84)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(-7.22,-6.28,0.08)
# Make the von Bertalanffy growth model
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=130, k=0.164, t0=-0.092, units=c("cm","yr-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
# Make a triangle copula for simulating process error
linf <- list(a=104.5, b=155.5, c=130) 
k <- list(a=0.132, b=0.196, c=0.164)
t0 <- list(a=-0.184, b=0, c=-0.092)
tri_pars <- list(linf = linf, k = k, t0 = t0)
# Simulate 10 iterations from it
vbObj_tri <- mvrtriangle(10, vbObj, paramMargins=tri_pars)
data(southernHakeLen)
# Extract the catch numbers at length from stock object
cth <- catch.n(shake_len) 
# Slice the data using the unsimulated growth object 
# so the stock and the growth object have 1 iteration
cthA1 <- l2a(cth, vbObj)
# Slice with 1 iteration in stock and multiple in growth object
cthA2 <- l2a(cth, vbObj_tri)
# Result is age based catch with multiple iterations
# mod: iter=1, data: iter=n
cthA3 <- l2a(propagate(cth,10), vbObj)
# both with iter=n
cthA4 <- l2a(propagate(cth,10), vbObj_tri)
# converting a stock object
shake_age <- l2a(shake_len, vbObj)
shake_age <- l2a(shake_len, vbObj_tri)
shake_age <- l2a(propagate(shake_len, 10), vbObj)
shake_age <- l2a(propagate(shake_len, 10), vbObj_tri)
# converting a index object
index_pt_age <- l2a(index_pt_len, vbObj)
index_pt_age <- l2a(index_pt_len, mvrnorm(10, vbObj))
index_pt_age <- l2a(propagate(index_pt_len, 10), vbObj)

Description

Method to compute natural mortality.

Usage

## S4 method for signature 'a4aM'
m(object, grMod = "missing", ...)

Arguments

Details

The method uses the range slot to define the quant and year dimensions of the resulting M FLQuant. The name for the quant dimension is taken as the name of a variable that is present in the shape formula, but not in the params slot of the shape model. If more than one such variable exists, then there is a problem with the shape model definition.

Value

an FLQuant object

Examples

age <- 0:15
k <- 0.4
shp <- eval(as.list(~exp(-age-0.5))[[2]], envir=list(age=age))
lvl <- eval(as.list(~1.5*k)[[2]], envir=list(k=k))
M <- shp*lvl/mean(shp)
# Now set up an equivalent a4aM object
mod1 <- FLModelSim(model=~exp(-age-0.5))
mod2 <- FLModelSim(model=~1.5*k, params=FLPar(k=0.4))
m1 <- a4aM(shape=mod1, level=mod2)
  # set up the age range for the object...
  range(m1, c("min", "max")) <- c(0,15)
  # ...and the age range for mbar
  range(m1, c("minmbar", "maxmbar")) <- c(0,15)
m(m1)
mean(m(m1)[ac(0:15)])
all.equal(M, c(m(m1)))

# another example m
range(m1, c("min", "max")) <- c(2,15)
range(m1, c("minmbar", "maxmbar")) <- c(2,4)
m(m1)
mean(m(m1)[ac(2:4)])

# example with specified iters (i.e. not simulated from a statistical distribution)...
mod2 <- FLModelSim(model=~k^0.66*t^0.57,
  params=FLPar(matrix(c(0.4,10,0.5,11), ncol=2, dimnames=list(params=c("k","t"), iter=1:2))),
  vcov=array(c(0.004,0.,0.,0.001,0.006,0.,0.,0.002), dim=c(2,2,2)))
m2 <- a4aM(shape=mod1, level=mod2)
range(m2, c("min", "max")) <- c(2,10)
m(m2)
# ...and with randomly generated iters (based on the medians for params(mod2) and vcov(mod2))
m3 <- a4aM(shape=mod1, level=mvrnorm(100, mod2))
range(m3, c("min", "max")) <- c(0,15)
m(m3)

# example with a trend
mod3 <- FLModelSim(model=~1+b*v, params=FLPar(b=0.05))
mObj <- a4aM(shape=mod1, level=mvrnorm(100, mod2), trend=mod3,
  range=c(min=0,max=15,minyear=2000,maxyear=2003,minmbar=0,maxmbar=0))
m(mObj, v=1:4)

Description

Method to average across a set of models. This is still experimental. Use with care.

Usage

ma(object, ...)

## S4 method for signature 'a4aFitSAs'
ma(object, stock, FUN, nsim = 1000)

Arguments

Value

an FLStock object with iterations defined by nsim

Examples

data(ple4)
data(ple4.indices)
fmod <- ~ factor(age) + s(year, k=20)
qmod <- c(list(~ s(age, k = 4)), rep(list(~s(age, k=4)), 5))
f1 <- sca(ple4, ple4.indices, fmodel=fmod, qmodel=qmod, fit = "assessment")
qmod <- c(list(~ s(age, k = 4) + year), rep(list(~s(age, k=4)), 5))
f2 <- sca(ple4, ple4.indices, fmodel=fmod, qmodel=qmod, fit = "assessment")
# AIC weighting
aicwt <- function(object){
 ICs <- -1 * sapply(object, AIC)
 exp( 0.5 * (ICs - max(ICs)))
}
stock.sim <- ma(a4aFitSAs(list(f1=f1, f2=f2)), ple4, aicwt, nsim = 100)
# equal weighting
eqwt <- function(object){
 v <- rep(1, length(object))
 names(v) <- names(object)
 v
}
stock.sim <- ma(a4aFitSAs(list(f1=f1, f2=f2)), ple4, eqwt, nsim = 100)

Description

Method to simulate multivariate normal parameters for an a4aM object.

Usage

## S4 method for signature 'numeric,a4aM,missing,missing,missing,missing'
mvrnorm(n = 1, mu)

Arguments

Value

an a4aM object with n iterations

Examples

mod1 <- FLModelSim(model=~exp(-age-0.5))
mod2 <- FLModelSim(model=~k^0.66*t^0.57, params=FLPar(matrix(c(0.4,10,0.5,11),
 ncol=2, dimnames=list(params=c("k","t"), iter=1:2))),
 vcov=array(c(0.004,0.,0.,0.001,0.006,0.,0.,0.003), dim=c(2,2,2)))
mod3 <- FLModelSim(model=~1+b*v, params=FLPar(b=0.05))
mObj <- a4aM(shape=mod1, level=mod2, trend=mod3,
 range=c(min=0,max=15,minyear=2000,maxyear=2003,minmbar=0,maxmbar=0))
mObj <- mvrnorm(100, mObj)
# Generate 100 iterations with no trend over time
  m(mObj, v=c(1,1,1,1))
# Generate replicates based on iteration-specific multivariate distributions
# (as defined by params() and vcov())
  params(mod2)
  vcov(mod2)
  m1<-mvrnorm(mod2)
  c(params(m1))
# Generate replicates based on a single multivariate distribution (here the
# median of params() and vcov() is used)
  mvrnorm(2,mod2)
  m2<-mvrnorm(2,mod2)
  c(params(m2))

Description

Simulates model parameters with user-defined copulas and marginals.

Usage

mvrcop(n, mvdc, ...)

## S4 method for signature 'numeric,FLModelSim'
mvrcop(n, mvdc, copula, ...)

Arguments

Value

an FLModelSim object with n groups of parameters

Examples

mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(100, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.1,0.0003)
md <- ~linf*(1-exp(-k*(t-t0)))
prs <- FLPar(linf=120, k=0.3, t0=0.1, units=c("cm","yr^-1","yr"))
vb <- FLModelSim(model=md, params=prs, vcov=mm, distr="norm")
pars <- list(list(a=90, b=125, c=120), list(a=0.2, b=0.4), list(a=0, b=0.4, c=0.1))
vbSim <- mvrcop(10000, vb, copula="archmCopula", family="clayton", param=2, 
   margins="triangle", paramMargins=pars)
boxplot(t(predict(vbSim, t=0:20+0.5)))
splom(data.frame(t(params(vbSim)@.Data)), pch=".")

Description

Method to generate multivariate parameters with user-defined copulas and marginals for a4aGr objects.

Usage

## S4 method for signature 'numeric,a4aGr'
mvrcop(n = 1, mvdc, ...)

Arguments

Value

an FLModelSim object with n groups of parameters

Examples

mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
pars <- list(list(a=50, b=100, c=58.5), list(a=0.06, b=0.2, c=0.086), list(a=0, b=0.005, c=0.001))
#In the following, the third, fourth and fifth arguments refer to the copula,
#  while the final two arguments refer to the marginal distributions:
vbObj <- mvrcop(10000, vbObj, copula="archmCopula", family="clayton", param=2, 
   margins="triangle", paramMargins=pars)
splom(data.frame(t(params(vbObj)@.Data)), pch=".")

Description

Method to generate multivariate normal parameters for a4aGr objects.

Usage

## S4 method for signature 'numeric,a4aGr,ANY,ANY,ANY,ANY'
mvrnorm(n = 1, mu)

Arguments

Value

an a4aGr object with n iterations

Examples

mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
vbObj <- mvrnorm(100,vbObj)

Description

Simulates model parameters using elliptical copulas and triangular marginals.

Usage

mvrtriangle(n, object, ...)

## S4 method for signature 'numeric,FLModelSim'
mvrtriangle(n = 1, object, ...)

Arguments

Value

an FLModelSim object with n sets of parameters

Examples

# Set up the FLModelSim object
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(100, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.1,0.0003)
md <- ~linf*(1-exp(-k*(t-t0)))
prs <- FLPar(linf=120, k=0.3, t0=0.1, units=c("cm","yr^-1","yr"))
vb <- FLModelSim(model=md, params=prs, vcov=mm, distr="norm")
# Simulate from a multivariate normal distribution...
  set.seed(1)
  vbSim <- mvrnorm(10000, vb)
  mm <- predict(vbSim, t=0:20+0.5)
#...from a multivariate triangular distribution with default ranges (0.01 and
#   0.99 quantiles for min and max using a normal distribution with mean from
#   params and sigma from vcov, and with the apex located at params)...
  set.seed(1)
  vbSim1 <- mvrtriangle(10000, vb)
  mm1 <- predict(vbSim1, t=0:20+0.5)
#...and from a multivariate triangular distribution with specified ranges 
#   (note if "c" is missing, it will take the average of "a" and "b")
  set.seed(1)
  pars <- list(list(a=90, b=125, c=120), list(a=0.2, b=0.4), list(a=0, b=0.4, c=0.1))
  vbSim2 <- mvrtriangle(10000, vb, paramMargins=pars)
  mm2 <- predict(vbSim2, t=0:20+0.5)
# Plot the results
par(mfrow=c(3,1))
boxplot(t(mm), main="normal")
boxplot(t(mm1), main="triangular")
boxplot(t(mm2), main="triangular2")
splom(data.frame(t(params(vbSim)@.Data)), pch=".")
splom(data.frame(t(params(vbSim1)@.Data)), pch=".")
splom(data.frame(t(params(vbSim2)@.Data)), pch=".")

Description

Method to generate multivariate parameters with elliptical copulas and triangular marginals for a4aGr objects.

Usage

## S4 method for signature 'numeric,a4aGr'
mvrtriangle(n = 1, object, ...)

Arguments

Details

The method is essentially a special case of mvrcop, where the copula is of type "ellipCopula" and family "t", and where the marginals are triangular.

Value

an a4aGr object with n iterations

Examples

# Set up the a4aGr object and parameters for the marginals
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
pars <- list(list(a=50, b=100, c=58.5), list(a=0.06, b=0.2, c=0.086), list(a=0, b=0.005, c=0.001))

# Note that mvrtriangle is a special case of mvrcop
set.seed(1)
vbObj1 <- mvrtriangle(10000, vbObj, paramMargins=pars, dispstr="ex", param=0)
set.seed(1)
vbObj2 <- mvrcop(10000, vbObj, copula="ellipCopula", family="t", 
	param=0, margins="triangle", paramMargins=pars)
all.equal(vbObj2, vbObj1)

Description

Checks that the name of the second dimension in params is "iter". For internal use, not very interesting for users. It takes a FLModelSim object and returns a logical.

Usage

pars2dim(object)

## S4 method for signature 'FLModelSim'
pars2dim(object)

## S4 method for signature 'FLPar'
pars2dim(object)

Arguments

pars2dim

Checks that the name of the second dimension in params is "iter". For internal use and not very interesting for users. It takes an FLPar object and returns a logical.

Examples

pars2dim(FLModelSim())
#Example use of pars2dim:
pars2dim(FLPar())
pars2dim(FLPar(array(dim=c(1,1,1))))

Description

Method to plot fitted versus observed catch numbers-at-age. Note the yaxis doesn't has a scale. The visual is about the difference between the two lines, not about the value of each line, which in any case would be very difficult to assess visually.

Usage

## S4 method for signature 'a4aFit,FLStock'
plot(x, y, ...)

Arguments

Value

a plot with fitted and observed catch numbers-at-age

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
plot(obj, ple4)

Description

Method to plot fitted versus observed indices-at-age. Note the yaxis doesn't has a scale. The visual is about the difference between the two lines, not about the value of each line, which in any case would be very difficult to assess visually.

Usage

## S4 method for signature 'a4aFit,FLIndices'
plot(x, y, ...)

Arguments

Value

a plot with fitted and observed indices-at-age

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
plot(obj, FLIndices(ple4.index))

Description

Method to produce scatterplots of standardized residuals

Method to produce scatterplots of standardized residuals

Usage

## S4 method for signature 'a4aFitResiduals,missing'
plot(x, y = missing, auxline = "smooth", ...)

## S4 method for signature 'a4aFitCatchDiagn,missing'
plot(x, y = missing, ...)

Arguments

Value

a plot with stardardized log residuals

a plot with stardardized log residuals

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))
plot(flqs)
data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))
plot(flqs)

Description

Predicts ages or lengths using a growth class

Usage

## S4 method for signature 'a4aGr'
predict(object, ...)

Arguments

Value

a matrix object with lengths or ages

Examples

# Set up the a4aGr object and parameters for the marginals
mm <- matrix(NA, ncol=3, nrow=3)
diag(mm) <- c(50, 0.001,0.001)
mm[upper.tri(mm)] <- mm[lower.tri(mm)] <- c(0.1,0.01,0.00004)
md <- ~linf*(1-exp(-k*(t-t0)))
imd <- ~t0-1/k*log(1-len/linf)
prs <- FLPar(linf=58.5, k=0.086, t0=0.001, units=c("cm","yr^-1","yr"))
vbObj <- a4aGr(grMod=md, grInvMod=imd, params=prs, vcov=mm, distr="norm")
predict(vbObj, len=1:50+0.5)
predict(vbObj, t=1:20+0.5)

Description

Predict methods for a4a stock assessment fits.

Usage

## S4 method for signature 'a4aFitSA'
predict(object)

## S4 method for signature 'SCAPars'
predict(object)

Arguments

Examples

data(ple4)
data(ple4.index)
fmodel <- ~factor(age) + factor(year)
qmodel <- list(~factor(age))
fit1 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))
flqs <- predict(fit1)

Description

Method to produce qqplots of standardized residuals

Usage

## S4 method for signature 'a4aFitResiduals,missing'
qqmath(x, data = missing, ...)

Arguments

Value

a qqplot with stardardized log residuals

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))
qqmath(flqs)

Description

Range method for a4aM objects

Usage

## S4 replacement method for signature 'a4aM,ANY,numeric'
range(x, i) <- value

Arguments

Description

Simulated length data for redfish. Simulations were done using GADGET.

Usage

data(rfLen)

Format

An FLStock.

Author(s)

Ernesto Jardim

Source

Daniel Howell

Description

Simulated stock based on red fish.

Usage

rfLen.stk

Format

an FLStock object

Author(s)

Daniel Howell

Description

Trawl survey index for red fish.

Usage

rfTrawl.idx

Format

an FLIndex object

Author(s)

Daniel Howell

Description

Trawl survey index for red fish, with a jump in catchability.

Usage

rfTrawlJmp.idx

Format

an FLIndex object

Author(s)

Daniel Howell

Description

Trawl survey index for red fish, with a trend in catchability.

Usage

rfTrawlTrd.idx

Format

an FLIndex object

Author(s)

Daniel Howell

Description

Statistical catch-at-age method of the a4a stock assessment framework.

Usage

sca(stock, indices, ...)

## S4 method for signature 'FLStock,FLIndex'
sca(stock, indices, ...)

## S4 method for signature 'FLStock,FLIndices'
sca(
  stock,
  indices,
  fmodel = missing,
  qmodel = missing,
  srmodel = missing,
  n1model = missing,
  vmodel = missing,
  covar = missing,
  wkdir = missing,
  verbose = FALSE,
  fit = "assessment",
  center = TRUE,
  mcmc = missing
)

Arguments

Details

[REQUIRES REVISION] This method is the advanced method for stock assessment, it gives the user access to a set of arguments that the sca method doesn't. In particular, the default for the fit argument is 'assessment'. For detailed information about using the sca read the vignette 'The a4a Stock Assessment Modelling Framework' (vignette('sca')).

Value

an a4aFit object if fit is "MP" or an a4aFitSA object if fit is "assessment"

Examples

data(ple4)
data(ple4.index)

# fishing mortality by age and year (separable) AND catchability at age without year trend
fmodel <- ~factor(age) + factor(year)
qmodel <- list(~factor(age))
fit1 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))

# fishing mortality as a smoother by age and year (but still separable) AND
# catchability at age without year trend
fmodel <- ~ s(age, k=4) + s(year, k=10)
qmodel <- list(~factor(age))
fit2 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))

# fishing mortality as a smoother by age and year (but still separable) AND
# catchability as a smoother by age without year trend
fmodel <- ~ s(age, k=4) + s(year, k=10)
qmodel <- list(~s(age, k=4))
fit3 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))

# fishing mortality as a smoother by age and year (but still separable) AND
# catchability as a smoother by age with year trend
fmodel <- ~ s(age, k=4) + s(year, k=10)
qmodel <- list(~s(age, k=4) + year)
fit4 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))

# It's a statistical model
BIC(fit1, fit2, fit3, fit4)

# fishing mortality as a smoother by age and year with interactions (i.e. non-separable) AND
# catchability as a smoother by age without year trend
fmodel <- ~ te(age, year, k=c(4, 10))
qmodel <- list(~s(age, k=4))
fit5 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))

# fit3 + smoother in recruitment
fmodel <- ~ s(age, k=4) + s(year, k=20)
qmodel <- list(~s(age, k=4))
rmodel <- ~s(year, k=20)
fit6 <-  sca(fmodel=fmodel, qmodel=qmodel, srmodel=rmodel, ple4, FLIndices(ple4.index))

# fit3 + bevholt
rmodel <- ~ bevholt(CV=0.05)
fit7 <-  sca(fmodel=fmodel, qmodel=qmodel, srmodel=rmodel, ple4, FLIndices(ple4.index))

Description

This function provides an interface to sca() to be used inside the mp() function of the mse package.

Usage

sca.sa(stk, idx, update = TRUE, dfm = c(0.75, 0.75), args, tracking, ...)

Arguments

Value

A list containing the estimated stock (stk, of class FLStock), and the tracking FLQuant, including convergence flags.

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

SCAMCMC(object, ...)

## S4 method for signature 'missing'
SCAMCMC(object, ...)

getADMBCallArgs(object, ...)

## S4 method for signature 'SCAMCMC'
getADMBCallArgs(object, ...)

getN(object, ...)

## S4 method for signature 'SCAMCMC'
getN(object, ...)

Arguments

Slot

mcmc N

Run N MCMC iterations

mcsave N

Save every N th MCMC iteration

mcscale N

Rescale step size for first N iterations

mcmult N

Rescale the covariance matrix

mcrb N

Reduce high parameter correlations

mcprobe X

Use a fat-tailed proposal distribution

mcdiag

Use a diagonal covariance matrix

mcnoscale

Do not scale the algorithm during

mcu

Use a uniform distribution as proposal distribution

hybrid

Use the hybrid method

hynstep N

Mean number of steps for the leapfrog method

hyeps X

The stepsize for the leapfrog method [X numeric and > 0]

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

SCAPars(object, ...)

## S4 method for signature 'missing'
SCAPars(object, ...)

stkmodel(object, ...)

## S4 method for signature 'SCAPars'
stkmodel(object)

n1model(object, ...)

## S4 method for signature 'SCAPars'
n1model(object)

srmodel(object, ...)

## S4 method for signature 'SCAPars'
srmodel(object)

fmodel(object, ...)

## S4 method for signature 'SCAPars'
fmodel(object)

qmodel(object, ...)

## S4 method for signature 'SCAPars'
qmodel(object)

qMod(object, ...)

## S4 method for signature 'SCAPars'
qMod(object)

vmodel(object, ...)

## S4 method for signature 'SCAPars'
vmodel(object)

vMod(object, ...)

## S4 method for signature 'SCAPars'
vMod(object)

srPars(object, ...)

## S4 method for signature 'SCAPars'
srPars(object)

srCovar(object, ...)

## S4 method for signature 'SCAPars'
srCovar(object)

srFrml(object, ...)

## S4 method for signature 'SCAPars'
srFrml(object)

fPars(object, ...)

## S4 method for signature 'SCAPars'
fPars(object)

fCovar(object, ...)

## S4 method for signature 'SCAPars'
fCovar(object)

fFrml(object, ...)

## S4 method for signature 'SCAPars'
fFrml(object)

qPars(object, ...)

## S4 method for signature 'SCAPars'
qPars(object)

qCovar(object, ...)

## S4 method for signature 'SCAPars'
qCovar(object)

qFrml(object, ...)

## S4 method for signature 'SCAPars'
qFrml(object)

vPars(object, ...)

## S4 method for signature 'SCAPars'
vPars(object)

vCovar(object, ...)

## S4 method for signature 'SCAPars'
vCovar(object)

vFrml(object, ...)

## S4 method for signature 'SCAPars'
vFrml(object)

## S4 method for signature 'SCAPars'
m(object)

## S4 method for signature 'SCAPars'
wt(object)

## S4 method for signature 'SCAPars'
propagate(object, iter, fill.iter = TRUE)

## S4 method for signature 'SCAPars'
iter(obj, it)

Arguments

Slot

stkmodel

parameters related to stock dynamics

qmodel

paramaters related to catchability of tunning fleets

vmodel

paramaters related to the variance model

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Description

This function provides an interface to a call to a separable model based on sca() to be used inside the mp() function of the mse package.

Usage

sep.sa(stk, idx, args, update = TRUE, dfm = c(0.75, 0.75), ...)

Arguments

Value

A list containing the estimated stock (stk, of class FLStock), and the tracking FLQuant, including convergence flags.

Description

FLR stock object for southern hake.

Usage

shake_len

Format

an FLStock object

Author(s)

Santiago Cervi\~no

Description

Simulation methods for a4a stock assessment fits.

Usage

simulate(object, nsim = 1, seed = NULL, ...)

## S4 method for signature 'a4aFitSA'
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)

## S4 method for signature 'SCAPars'
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)

## S4 method for signature 'a4aStkParams'
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)

## S4 method for signature 'submodels'
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)

## S4 method for signature 'submodel'
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)

Arguments

Examples

data(ple4)
data(ple4.index)
fmodel <- ~factor(age) + factor(year)
qmodel <- list(~factor(age))
fit1 <-  sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))
fit1
summary(fit1)
stock.n(fit1)

Description

Length based stock and three indices data for Southern hake.

Usage

data(southernHakeLen)

Format

an FLStockLen and three FLIndex objects.

Author(s)

Finlay Scott

Source

Santiago Cervino

Description

Method to compute the standardized residuals on the log scale for index- and catch-at-age residuals in the a4a stock assessment framework.

Usage

stdlogres(obs, fit, ...)

## S4 method for signature 'FLQuant,FLQuant'
stdlogres(obs, fit, ...)

Arguments

Value

an FLQuant with stardardized log residuals

Examples

data(ple4)
data(ple4.index)
obj <- sca(ple4, FLIndices(ple4.index))
flqs <- residuals(obj, ple4, FLIndices(idx=ple4.index))
stdlogres(catch.n(ple4), catch.n(obj))
# which is the same as the following (because residuals() uses stdlogres):
flqs$catch.n
# check:
stdlogres(catch.n(ple4),catch.n(obj)) - flqs$catch.n

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

submodel(object, ...)

## S4 method for signature 'missing'
submodel(object, ...)

## S4 method for signature 'submodel'
params(object)

sMod(object, ...)

## S4 method for signature 'submodel'
sMod(object)

## S4 method for signature 'submodel'
iter(obj, it)

## S4 method for signature 'submodel'
propagate(object, iter, fill.iter = TRUE)

## S4 method for signature 'submodel'
formula(x)

Arguments

Slot

Mod

formula describing the model

params

FLPar with model parameters

vcov

array with variance covariance paramaters related to the variance model

centering

numeric value used for centering the data

distr

a character with the parameters' statistical distribution; it must match a known distribution for R (e.g. "norm" for gaussian) so that rnorm can be called

Accessors

All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.

The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Description

Class definition (slots), constructors, accessors, replacement (when relevant) and common methods.

Usage

submodels(...)

submodels(...)

corBlocks(object, ...)

## S4 method for signature 'submodels'
corBlocks(object)

## S4 method for signature 'submodels'
params(object)

## S4 method for signature 'submodels'
sMod(object)

## S4 method for signature 'submodels'
formula(x)

corBlocks(object, ...) <- value

## S4 replacement method for signature 'submodels,list'
corBlocks(object, ...) <- value

## S4 replacement method for signature 'submodels,submodel'
x$name <- value

## S4 replacement method for signature 'submodels,character,missing'
x[[i, j, ...]] <- value

## S4 replacement method for signature 'submodels,numeric,missing'
x[[i, j, ...]] <- value

## S4 method for signature 'submodels'
propagate(object, iter, fill.iter = TRUE)

## S4 method for signature 'submodels'
iter(obj, it)

Arguments

Constructor

A construction method exists for this class that can take named arguments for any of its slots. All slots are then created to match the requirements of the class validity. If an unnamed FLQuant object is provided, this is used for sizing, but not for populating any slot.

Note

This class is similar to other 'plural' calsses in FLR. It is a list constrained to having all elements of the same class, in this case submodel. Otherwise it works exacly as any other list.

Description

Methods to extract and replace the variance-covariance matrix.

Usage

## S4 method for signature 'a4aFitSA'
vcov(object)

## S4 method for signature 'SCAPars'
vcov(object)

## S4 method for signature 'submodels'
vcov(object)

## S4 method for signature 'submodel'
vcov(object)

## S4 replacement method for signature 'a4aFitSA,numeric'
vcov(object, ...) <- value

## S4 replacement method for signature 'SCAPars,numeric'
vcov(object, ...) <- value

## S4 replacement method for signature 'a4aStkParams,numeric'
vcov(object, ...) <- value

## S4 replacement method for signature 'submodel,numeric'
vcov(object, ...) <- value

## S4 replacement method for signature 'submodel,matrix'
vcov(object, ...) <- value

## S4 replacement method for signature 'submodel,array'
vcov(object, ...) <- value

Arguments

Description

Method to 3D plot FLQuant objects.

Usage

## S4 method for signature 'FLQuant,missing'
wireframe(x, y, screen = list(x = -90, y = -45), ...)

Arguments

Value

a 3D surface plot

Examples

data(ple4)
wireframe(harvest(ple4))