sca.RdStatistical catch-at-age method of the a4a stock assessment framework.
sca(stock, indices, ...)
# S4 method for FLStock,FLIndex
sca(stock, indices, ...)
# S4 method for 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
)an FLStock object containing catch and stock information
an FLIndices object containing survey indices
additional argument list that might never be used
a formula object depicting the model for log fishing mortality at age
a list of formula objects depicting the models for log survey catchability at age
a formula object depicting the model for log recruitment
a formula object depicting the model for the population in the first year of the time series
a list of formula objects depicting the model for the variance of fishing mortality and the indices
a list with covariates to be used by the submodels. The formula must have an element with the same name as the list element.
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.
if true, admb fitting information is printed to the screen.
character with type of fit: 'MP' or 'assessment'; the former does not require the hessian to be computed, while the latter does.
logical defining if the data should be centered before fitting.
an SCAMCMC object with the arguments to run MCMC
an a4aFit object if fit is "MP" or an a4aFitSA object if fit is "assessment"
[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')).
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)
#> df BIC
#> fit1 148 792.2708
#> fit2 91 662.5661
#> fit3 85 629.2156
#> fit4 86 471.2773
# 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))