ma-methods.RdMethod to average across a set of models. This is still experimental. Use with care.
ma(object, ...)
# S4 method for a4aFitSAs
ma(object, stock, FUN, nsim = 1000)an a4aFits object with the fits to be averaged across
additional argument list that might never be used
a stock object with the original data used for fitting
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()
a numeric with the number of simulations to be drawn
an FLStock object with iterations defined by nsim
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)
#> Warning: This method is experimental, use at your own risk !
#> model weights are
#> weight..perc.
#> f1 0.665
#> f2 99.335
# 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)
#> Warning: This method is experimental, use at your own risk !
#> model weights are
#> weight..perc.
#> f1 50
#> f2 50