Enrico gave you a workaround that stores the extra values in an environment.
Another possible workaround is an optional argument to myfun() that asks
it to return more information, e.g.
fr <- function(x, data, extraInfo = FALSE) { ## Rosenbrock Banana
function
x1 <- x[1]
x2 <- x[2]
ans <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
if (extraInfo) {
list(ans=ans, extras = ...)
else
ans
}
Then after optim() finishes, call fr() again with parameters as returned
by optim, and extraInfo = TRUE.
Duncan Murdoch
On 03/08/2023 4:21 p.m., Sami Tuomivaara wrote:
Dear all,
I have used optim a lot in contexts where it would useful to be able to iterate
function myfun that, in addition to the primary objective to be minimized
('minimize.me'), could return other values such as alternative metrics of the
minimization, informative intermediate values from the calculations, etc.
myfun <- function()
{
...
return(list(minimize.me = minimize.me, R2 = R2, pval = pval, etc.))
}
During the iteration, optim could utilize just the first value from the myfun
return list; all the other values calculated and returned by myfun could be
ignored by optim.
After convergence, the other return values of myfun could be finally extracted and
appended into the optim return value (which is a list) as additional entry e.g.:
$aux <- list(R2, pval, etc.), (without 'minimize.me' as it is already returned
as $value).
The usual ways for accessing optim return values, e.g., $par, $value, etc. are
not affected. Computational cost may not be prohibitive either. Is this
feasible to consider?
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