Package 'FLa4a'

Title: A Simple and Robust Statistical Catch at Age Model
Description: A simple and robust statistical Catch at Age model that is specifically designed for stocks with intermediate levels of data quantity and quality.
Authors: Colin P. Millar [aut], Ernesto Jardim [aut, cre]
Maintainer: Ernesto Jardim <[email protected]>
License: EUPL
Version: 1.9.6
Built: 2026-05-22 18:55:26 UTC
Source: https://github.com/flr/FLa4a

Help Index

S4 class a4aFit

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'
window(
  x,
  start = dims(x)$minyear,
  end = dims(x)$maxyear,
  extend = TRUE,
  frequency = 1
)

a4aFits(object, ...)

## S4 method for signature 'list'
a4aFits(object, ...)

## S4 method for signature 'a4aFit'
a4aFits(object, ...)

## S4 method for signature 'missing'
a4aFits(object, ...)

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

Arguments

...

additional argument list that might never be used
object

object of relevant class (see signature of method)
obj

the object to be subset
it

iteration to be extracted
x

the object to be subset
start

initial year
end

final year
extend

if object is shorter than end-start extend to cover year range
frequency

interval between years if extended

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)

S4 class a4aFitCatchDiagn

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
stock

FLStock object used to fit the model
indices

FLIndices object used to fit the model

Examples

data(ple4)
data(ple4.index)
fit <- sca(ple4, ple4.index)
flqs <- computeCatchDiagnostics(fit, ple4)

S4 class a4aFitMCMC

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)

a4aFitMCMCs(object, ...)

## S4 method for signature 'list'
a4aFitMCMCs(object, ...)

## S4 method for signature 'a4aFitMCMC'
a4aFitMCMCs(object, ...)

## S4 method for signature 'missing'
a4aFitMCMCs(object, ...)

Arguments

...

additional argument list that might never be used
object

object of relevant class (see signature of method)
x

an object to be coerced into mcmc
burnin

a numeric with the number of iterations to be removed

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))

S4 class a4aFitResiduals

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

object

object of relevant class (see signature of method)
stock

FLStock object used to fit the model
indices

FLIndices object used to fit the model
...

additional argument list that might never be used

Examples

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

S4 class a4aFitSA

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

...

additional argument list that might never be used
object

object of relevant class (see signature of method)
obj

the object to be subset
it

iteration to be extracted

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))

Individual growth class

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
value

the new object

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")

Stock assessment model advanced method

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

stock

an FLStock object containing catch and stock information
indices

an FLIndices object containing survey indices
fmodel

a formula object depicting the model for log fishing mortality at age
qmodel

a list of formula objects depicting the models for log survey catchability at age
srmodel

a formula object depicting the model for log recruitment
n1model

a formula object depicting the model for the first year of catch data
vmodel

a list of formula objects depicting the models for log survey and log fishing mortality variance
covar

a list with covariates
wkdir

used to set a working directory for the admb optimiser. If wkdir is set all admb files are saved to this folder otherwise they are deleted.
verbose

if true admb fitting information is printed to the screen
fit

character with type of fit: 'MP' or 'assessment'; the former doesn't require the hessian to be computed, while the latter does.
center

logical specifying whether data is centered before estimating or not
mcmc

SCAMCMC specifying parameters for the ADMB MCMC run, check ADMB manual for detailed description

Value

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

Natural mortality class

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
value

the new object

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)

Stock parameters class

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
value

the new object
iter

the number of iterations to create
fill.iter

should the new iterations be filled with values (TRUE) or NAs (FALSE)
obj

the object to be subset
it

iteration to be extracted

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.

+ methods

Description

Update FLStock and FLIndex objects with stock assessment results.

Update FLStocks objects with multiple stock assessment results in a a4aFits.

Add residual uncertainty to FLStock objects.

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

Usage

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

## S4 method for signature 'FLStocks,a4aFits'
e1 + e2

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

Arguments

e1

the original FLStock or FLIndex object
e2

a a4aFit object from where the new FLStock or FLIndex slots will be extracted.

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.

If both objects have the same number of iterations, the FLStocks slots will be replaced by the a4aFits 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.

Random normal draws will be added to the log transformed stock.n and catch.n slots using the standard deviation computed from the residuals catch.n and index, for catch.n and stock.n respectively. Returns a FLStock object.

Assorted methods needed by FLa4a

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 'FLI'
replaceZeros(object, fraction = 0.25)

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

Arguments

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
year

numeric with year to be extracted
obj

an object
fraction

numeric with fraction of minimum to replace zeros

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

Breakpoints

Description

Method to set breakpoints in submodels

Usage

breakpts(var, ...)

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

Arguments

var

a numeric object that defines the variable to be "broken"
...

additional argument list that might never be used
breaks

a numeric object that defines the breakpoints

Value

a factor with levels according to the defined breaks

Bubbles plot of standardized log residuals

Description

Method to produce bubble plots of standardized residuals

Usage

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

Arguments

x

an a4aFitResiduals object with the standardized residuals
data

ignored
...

additional argument list that might never be used

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)

Collapse seasons

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

stock

an FLStock object

Value

a FLStock object

Default sub-models

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

stock

an FLStock object
dfm

numeric vector with the data points fraction to be used to set the spline ks.
indices

an FLIndices object

Value

a FLStock object

deprecated

Description

Deprecated methods.

Usage

a4aSCA(...)

Arguments

...

additional argument list that might never be used

dps567

Description

FLR stock object.

Usage

dps567

Format

an FLStock object

Author(s)

GFCM

dps567.idx

Description

FLR index object.

Usage

dps567.idx

Format

an FLIndex object

Author(s)

GFCM

coefficients extract and replacement

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

object

object of relevant class (see signature of method)
value

the new object
...

additional argument list that might never be used

Methods to generate FLIndex objects

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

object

an FLIndex object
...

additional argument list that might not ever be used.
cv

the coefficient of variation
niter

the number of iterations to be generated

Value

an FLIndex

Methods to generate FLQuant objects

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

object

an FLQuant
...

additional argument list that might not ever be used.
cv

the coefficient of variation
method

the method used to compute the correlation matrix; for now only "ac" (autocorrelation) is implemented
niter

the number of iterations to be generated
type

the type of output required. The default is on the scale of the linear predictors (link); the alternative "response" is on the scale of the response variable. Thus for a model on the log scale the default predictions are of log F (for example) and type = "response" gives the predicted F.
nsim

the number of iterations to simulate, if nsim = 0, then deterministic values are returned based on the coefficients. If nsim > 0 then coefficients are simluated using the covariance slot and distribution slot.
seed

if supplied the random numbers are generate with a fixed seed for repeatablility
simulate.recruitment

if FALSE (default) recruitment is simulated from the recruitment estimates of recruitment, which may or may not be based on a stock-recruit model in the origional fit. If TRUE, then new recruitments are simulated based on the stock recruitment model and supplied CV used in the fit, rsulting in a completly different timeseries of N and Catches.

Value

an FLQuant

Examples

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

Methods to generate FLStock objects

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

object

an FLStock
R

an FLQuant with iterations or missing
C

an FLQuant with iterations or missing
F

an FLQuant with iterations or missing
...

additional argument list that might not ever be used.

Value

an FLStock

compute log-correlation matrix

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used

Value

an FLQuant object with a quant log-correlation matrix

Examples

data(ple4)
getAcor(harvest(ple4))

Get ADMB Hessian

Description

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

Usage

getADMBHessian(wkdir)

getADMBCovariance(wkdir)

Arguments

wkdir

the location of the admb output

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)

Get covariance matrix

Description

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

Usage

getCov(n, model, tau)

Arguments

n

integer giving the size of the random feild
model

chatacter giving the name of the GMRF
tau

numeric giving the multiplier of the structure matrix for the model

Value

a covariance matrix

Get K

Description

Method to get values of the growth parameter K

Usage

getK(object, ...)

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

Arguments

object

an a4aGr object
...

additional argument list that might never be used

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)

Get TPL with ADMB code

Description

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

Usage

getTPL(dir)

Arguments

dir

folder where the a4a.tpl file will be copied to.

Value

file a4a.tpl

Examples

getTPL("myfolder")

Get model matrix

Description

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

Usage

getX(object, ...)

## S4 method for signature 'formula'
getX(object, df, newdf = df, tol = 1e-04)

Arguments

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
df

the data.frame to build the model matrix against.
newdf

the data.frame to create the model matrix for.

Value

a matrix.

Note

getX is intended to be used internally

hakeGSA7

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

hakeGSA7.idx

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

hke1567

Description

FLR stock object.

Usage

hke1567

Format

an FLStock object

Author(s)

GFCM

hke1567.idx

Description

FLR index object.

Usage

hke1567.idx

Format

an FLIndex object

Author(s)

GFCM

index_cd_len

Description

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

Usage

index_cd_len

Format

an FLIndex object

Author(s)

Santiago Cervino

index_pt_len

Description

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

Usage

index_pt_len

Format

an FLIndex object

Author(s)

Santiago Cervino

index_sp_len

Description

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

Usage

index_sp_len

Format

an FLIndex object

Author(s)

Santiago Cervino

Method to convert length-based data to age-based

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

object

an FLQuant, or FLStockLen object.
model

an a4aGr object
...

additional argument list that might never be used
halfwidth

the halfwidths of the length classes; a single numeric or vector the size of the number of length classes; not used if object is an FLStockLen
stat

the aggregation statistic, which must be mean or sum; only used if object is an FLQuant.
max_age

the maximum age in the returned FLQuant; all ages above this are set to max_age; only used if object is an FLQuant
plusgroup

the plusgroup of the stock; only used if the object is an FLStockLen.

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)

natural mortality

Description

Method to compute natural mortality.

Usage

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

Arguments

object

a a4aM object
grMod

a a4aGr object from which the growth parameter K can be extracted
...

placeholder for covariates of the models. The names must match formula variables (not parameters), with the exception of the a4aGr individual growth model. To use a growth model, it must be called grMod and be of class a4aGr, in which case the parameters will be matched. The main objective is to be able to use K from von Bertalanffy models in M.

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)

Model averaging (experimental)

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

object

an a4aFits object with the fits to be averaged across
...

additional argument list that might never be used
stock

a stock object with the original data used for fitting
FUN

a function to compute the weights, which must return a named vector with weights. Note the weights will be normalized to sum 1 by ma()
nsim

a numeric with the number of simulations to be drawn

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)

mut09

Description

FLR stock object.

Usage

mut09

Format

an FLStock object

Author(s)

GFCM

mut09.idx

Description

FLR index object.

Usage

mut09.idx

Format

an FLIndex object

Author(s)

GFCM

natural mortality

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

n

the number of iterations to be generated
mu

an a4aM object

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))

Simulation using copula models

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

n

the number of iterations
mvdc

an FLModelSim object
...

arguments to be passed to the copula methods
copula

the name of the copula to be used

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=".")

mvrcop

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

n

the number of iterations
mvdc

the a4aGr object
...

arguments to be passed to the rMvdc and copula methods

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=".")

mvrnorm

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

n

the number of simulations to be generated
mu

an a4aGr object

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)

Simulation with a copula model and triangular distributions

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

n

the number of iterations
object

the FLModelSim object
...

arguments to be passed to the rMvdc and copula methods

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=".")

mvrtriangle

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

n

the number of iterations
object

object of relevant class (see signature of method)
...

additional argument list that might never be used

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)

Check that the second dimension in params is "iter"

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

object

object of relevant class (see signature of method)

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))))

plot for fitted catch-at-age

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

x

an a4aFit object with the fitted values
y

an FLStock object with the observed values
...

additional argument list that might never be used

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)

Plot for fitted indices-at-age

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

x

an a4aFit object with the fitted values
y

an FLIndices object with the observed values
...

additional argument list that might never be used

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))

Plot of metrics of multiple fits

Description

Method to plot fitting statistics of multiple fits, useful to compare fits.

Usage

## S4 method for signature 'a4aFits,missing'
plot(x, y = missing, ...)

Arguments

x

an a4aFits object with multiple fits
y

ignored
...

additional argument list that might never be used

Value

a plot with fitting statistics

Examples

data(ple4)
data(ple4.index)
qmods <- list(list(~s(age, k=6)))
fmods = list()
for(i in 1:6) {
  fmods[[paste0(i)]] <-
    as.formula(
      paste0("~te(age, year, k = c(6,", i+14,"), bs = 'tp') + s(age, k = 6)")
    )
}
myFits <-
  scas(
    FLStocks(ple4), list(FLIndices(ple4.index)),
    fmodel = fmods, qmodel=qmods, fit="MP"
  )
plot(myFits)

Plot of metrics of multiple fits

Description

Method to plot fitting statistics of multiple fits, useful to compare fits.

Usage

## S4 method for signature 'a4aFitMCMCs,missing'
plot(x, y = missing, ...)

Arguments

x

an a4aFitMCMCs object with multiple fits
y

ignored
...

additional argument list that might never be used

Value

a plot with fitting statistics

Examples

data(ple4)
data(ple4.index)
qmods <- list(list(~s(age, k=6)))
fmods = list()
for(i in 1:6) {
  fmods[[paste0(i)]] <-
    as.formula(
      paste0("~te(age, year, k = c(6,", i+14,"), bs = 'tp') + s(age, k = 6)")
    )
}
myFits <-
  scas(
    FLStocks(ple4), list(FLIndices(ple4.index)),
    fmodel = fmods, qmodel=qmods, fit="MCMC", mcmc=SCAMCMC()
  )
plot(myFits)

Plot of aggregated catch standardized log residuals

Description

Method to produce scatterplots of aggregated catch residuals

Usage

## S4 method for signature 'a4aFitCatchDiagn,missing'
plot(x, y = missing, probs = c(0.1, 0.9), type = "all", ...)

Arguments

x

an a4aFit object with the model fit
y

the FLStock object used to fit the model
...

additional argument list that might never be used

Value

a plot with stardardized log residuals

Examples

data(ple4)
data(ple4.index)
fit <- sca(ple4, ple4.index)
flqs <- computeCatchDiagnostics(fit, ple4)
plot(flqs)

Plot of standardized log residuals

Description

Method to produce scatterplots of standardized residuals

Usage

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

Arguments

x

an a4aFitResiduals object with the standardized residuals
y

ignored
auxline

a string defining the type of line to be added, by default uses 'smooth', a common alternative is to use 'r', a regression, or leave it empty ”
by

a string defining the type of plot to be depicted. At the moment accepts only 'age', the default, or 'year'.
...

additional argument list that might never be used

Value

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)

predict for a4aGr

Description

Predicts ages or lengths using a growth class

Usage

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

Arguments

object

the a4aGr object
...

arguments to be passed to the rMvdc and copula methods

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)

Predict methods for SCA

Description

Predict methods for a4a stock assessment fits.

Usage

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

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

Arguments

object

object of relevant class (see signature of method)

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)

qqplot of standardized log residuals

Description

Method to produce qqplots of standardized residuals

Usage

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

Arguments

x

an a4aFitResiduals object with the standardized residuals
data

ignored
...

additional argument list that might never be used

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)

range for a4aM objects

Description

Range method for a4aM objects

Usage

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

Arguments

x

an a4aM object
i

the elements of range to be changed in a character vector
value

a numeric vector with values

redfish length data

Description

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

Usage

data(rfLen)

Format

An FLStock.

Author(s)

Ernesto Jardim

Source

Daniel Howell

rfLen.stk

Description

Simulated stock based on red fish.

Usage

rfLen.stk

Format

an FLStock object

Author(s)

Daniel Howell

rfTrawl.idx

Description

Trawl survey index for red fish.

Usage

rfTrawl.idx

Format

an FLIndex object

Author(s)

Daniel Howell

rfTrawlJmp.idx

Description

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

Usage

rfTrawlJmp.idx

Format

an FLIndex object

Author(s)

Daniel Howell

rfTrawlTrd.idx

Description

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

Usage

rfTrawlTrd.idx

Format

an FLIndex object

Author(s)

Daniel Howell

Statistical catch-at-age method

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

stock

an FLStock object containing catch and stock information
indices

an FLIndices object containing survey indices
...

additional argument list that might never be used
fmodel

a formula object depicting the model for log fishing mortality at age
qmodel

a list of formula objects depicting the models for log survey catchability at age
srmodel

a formula object depicting the model for log recruitment
n1model

a formula object depicting the model for the population in the first year of the time series
vmodel

a list of formula objects depicting the model for the variance of fishing mortality and the indices
covar

a list with covariates to be used by the submodels. The formula must have an element with the same name as the list element.
wkdir

used to set a working directory for the admb optimiser; if wkdir is set, all admb files are saved to this folder, otherwise they are deleted.
verbose

if true, admb fitting information is printed to the screen.
fit

character with type of fit: 'MP' or 'assessment'; the former does not require the hessian to be computed, while the latter does.
center

logical defining if the data should be centered before fitting.
mcmc

an SCAMCMC object with the arguments to run MCMC

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))

Call sca inside the mp function

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

stk

The FLStock input object.
idx

The FLIndices input object.
update

Should the fmodel be updated with the default?
dfm

data points fraction to be used to set the spline ks.
...

Any other arguments to sca()
genArgs

The mse arguments used by mp().

Value

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

MCMC settings class

Description

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

Usage

SCAMCMC(object, ...)

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

getADMBCallArgs(object, ...)

getN(object, ...)

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

Arguments

object

a SCAMCMC object
...

extra 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.

Model parameters class

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
iter

the number of iterations to create
fill.iter

should the new iterations be filled with values (TRUE) or NAs (FALSE)
obj

the object to be subset
it

iteration to be extracted

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.

Run several stock assessments in a single run

Description

Internal method to run several stock assessment fits with different stocks, indices and submodels

Usage

scas(
  stocks,
  indicess,
  fmodel = missing,
  qmodel = missing,
  srmodel = missing,
  n1model = missing,
  vmodel = missing,
  combination.all = FALSE,
  workers = 1,
  ...
)

Arguments

stocks

an FLStocks object containing catch and stock information
indicess

a list of FLIndices objects containing survey indices
fmodel

a list of fmodel objects, each with a formula object depicting the model for log fishing mortality at age
qmodel

a list of qmodel objects, each with a list of formula objects depicting the models for log survey catchability at age
srmodel

a list of srmodel objects, each with a formula object depicting the model for log recruitment
n1model

a list of n1model objects, each with a formula object depicting the model for the first year of catch data
vmodel

a list of vmodel objects, each with a list of formula objects depicting the models for log survey and log fishing mortality variance
combination.all

bolean parameter (default is FALSE) to define if a full factorial across all stocks, indices, and submodel is run or just a sequence of runs.
...

all other arguments to be passed to sca
stock

an FLStocks object, each component with a FLStock object containing catch and stock information

Value

an a4aFits or a4aFitSAs or a4aFitMCMCs depending on the argument fit

Call a separable SA inside the mp function

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

stk

The FLStock input object.
idx

The FLIndices input object.
update

Should the fmodel be updated with the default?
dfm

data points fraction to be used to set the spline ks.
...

Any other arguments to sca()
genArgs

The mse arguments used by mp().

Value

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

shake_len

Description

FLR stock object for southern hake.

Usage

shake_len

Format

an FLStock object

Author(s)

Santiago Cervino

Simulation methods for SCA

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, obserror = FALSE)

## 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

object

object of relevant class (see signature of method)
nsim

number of iterations
seed

numeric with random number seed
...

additional argument list that might never be used
empirical

logical, shall the empirical method in MASS be used

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)

Southern hake length data

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

Standardized log residuals

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

obs

an FLQuant object with the observations
fit

an FLQuant object with the fitted value
...

additional argument list that might never be used

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

Submodel class

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
obj

the object to be subset
it

iteration to be extracted
iter

the number of iterations to create
fill.iter

should the new iterations be filled with values (TRUE) or NAs (FALSE)
x

the submodel object that is to be modified

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.

Submodels class

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

...

additional argument list that might never be used
object

object of relevant class (see signature of method)
x

object to be modified
value

value the new object
name

name(s) of entry to be extracted / modified
i, j

indices specifying elements to extract or replace.
iter

the number of iterations to create
fill.iter

should the new iterations be filled with values (TRUE) or NAs (FALSE)
obj

the object to be subset
it

iteration to be extracted

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.

Variance-covariance matrix

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

object

object of relevant class (see signature of method)
...

additional argument list that might never be used
value

the new object

wireframe plot for FLQuant

Description

Method to 3D plot FLQuant objects.

Usage

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

Arguments

x

a FLQuant
y

missing
screen

list with numeric components 'x','y' and 'z' to change the 3D perspective
...

additional argument list for the lattice engine

Value

a 3D surface plot

Examples

data(ple4)
wireframe(harvest(ple4))