On 2025-12-02 7:41 a.m., Therneau, Terry M., Ph.D. via R-devel wrote:
I have a complex likelihood function f() to maximize, with lots of arguments
(some of which set up indexes for derivatives, for instance).
When using something like optim(), one can pass these arguments through via its
� arg, or could make the likelihood function f() live in the same environment
as the main routine so they are found directly. Is there any advantage of
one versus the other wrt speed? At the end of the day, f() may get called
thousands of times in a Hamiltonian MCMC.
Since R does not replicate arguments that are used in a read-only fashion, one
might expect little to no penalty for having them on the call chain, unless the
bookkeeping for copy-on-write is itself time consuming.
By the way, a nice way to put the args in the environment of the
objective function is to use local() or a builder, e.g.
objective <- local({
arg1 <- 1
arg2 <- 2
arg3 <- 3
function(x) {
# objective code here that can see arg1, arg2, arg3
}
})
or
makeObjective <- function(arg1, arg2, arg3) {
force(arg1) # evaluate the promises
force(arg2)
force(arg3)
function(x) {
# objective code here that can see arg1, arg2, arg3
}
}
objective <- makeObjective(1,2,3)
Duncan Murdoch
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
