a4aFitSAa4aFitSA-class.RdThe a4aFitSA class extends a4aFit to store information about the parameters of the model.
a4aFitSA(...)
a4aFitSA(...)
# S4 method for a4aFitSA
a4aFit(object, ...)
pars(object)
# S4 method for a4aFitSA
pars(object)
# S4 method for a4aFitSA
m(object)
# S4 method for a4aFitSA
wt(object)
# S4 method for a4aFitSA
qmodel(object)
# S4 method for a4aFitSA
fmodel(object)
# S4 method for a4aFitSA
srmodel(object)
# S4 method for a4aFitSA
n1model(object)
# S4 method for a4aFitSA
vmodel(object)
# S4 method for a4aFitSA
stkmodel(object)
# S4 method for a4aFitSA
show(object)
# S4 method for a4aFitSA
submodels(object, ...)
# S4 method for a4aFitSA
iter(obj, it)
a4aFitSAs(object, ...)
# S4 method for list
a4aFitSAs(object, ...)
# S4 method for a4aFitSA
a4aFitSAs(object, ...)
# S4 method for missing
a4aFitSAs(object, ...)additional argument list that might never be used
object of relevant class (see signature of method)
the object to be subset
iteration to be extracted
The function call
Information on call duration
Fit summary
Estimates of stock numbers-at-age
Estimates of fishing mortality at age
Estimates of catch numbers-at-age
Estimates of survey or CPUE indices-at-age
an object of class SCAPars with information about model parameters
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.
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.
data(ple4)
data(ple4.index)
obj <- sca(stock=ple4, indices=FLIndices(ple4.index), fit="assessment")
obj
#> a4a model fit for: PLE
#>
#> Call:
#> .local(stock = stock, indices = indices, fit = "assessment")
#>
#> Time used:
#> Pre-processing Running a4a Post-processing Total
#> 0.7331221 9.3698771 0.4177635 10.5207627
#>
#> Submodels:
#> fmodel: ~te(age, year, k = c(6, 30), bs = "tp") + s(age, k = 6)
#> srmodel: ~factor(year)
#> n1model: ~s(age, k = 3)
#> qmodel:
#> BTS-Combined (all): ~s(age, k = 6)
#> vmodel:
#> catch: ~s(age, k = 3)
#> BTS-Combined (all): ~1
slotNames(obj)
#> [1] "pars" "call" "clock" "fitSumm" "stock.n" "harvest" "catch.n"
#> [8] "index" "name" "desc" "range"
clock(obj)
#> Pre-processing Running a4a Post-processing Total
#> 0.7331221 9.3698771 0.4177635 10.5207627
fitSumm(obj)
#> iters
#> 1
#> nopar 2.540000e+02
#> nlogl -1.008543e+03
#> maxgrad 1.278680e-03
#> nobs 8.300000e+02
#> gcv 8.858002e-02
#> convergence 0.000000e+00
#> accrate NA
#> nlogl_comp1 -1.063960e+03
#> nlogl_comp2 5.541910e+01
flq <- stock.n(obj)
is(flq)
#> [1] "FLQuant" "FLArray" "array" "structure" "vector"
flq <- index(obj)
is(flq)
#> [1] "FLQuants" "FLlst" "list" "vector"
logLik(obj)
#> 'log Lik.' 1008.543 (df=254)
AIC(obj)
#> [1] -1509.087
BIC(obj)
#> [1] -309.8444
is(pars(obj))
#> [1] "SCAPars"