R help - Aug 2004 - how to efficiently bootstrap mgcv::gam

Hi,
 
I repeatedly estimate the same gam model (package mgcv) using different
subsets of the data. A naive approach is, of course, to estimate the
model from scratch for each of the subsets. However I wonder if there
are some computations that can be "factored out" for the sake of
efficiency?
 
To be specific here is a stylized example of what I do now (which I'd
like to make more efficient):
 
gamA <- vector("list", 100)
 
for (i in seq(length(gamA))) {
  gamA[[i]] <- gam(y ~ x2 + s(x1, by=x2), data=myData,
subset=sample(nrow(myData), 10000))
}
 
 
Thanks,
Vadim

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