suggested modification to the 'mle' documentation?

Ben Bolker wrote:

  At this point I'd just like to advertise the "bbmle" package
(on CRAN) for those who respectfully disagree, as I do, with Peter over
this issue.  I have added a data= argument to my version
of the function that allows other variables to be passed
to the objective function.  It seems to me that this is perfectly
in line with the way that other modeling functions in R
behave.

This is at least cleaner than abusing the "fixed" argument. As you know,
I have reservations, one of which is that it is not a given that I want
it to behave just like other modeling functions, e.g. a likelihood
function might refer to more than one data set, and/or data that are not
structured in the traditional data frame format. The design needs more
thought than just adding arguments.

I still prefer a design based a plain likelihood function. Then we can
discuss how to construct such a function so that  the data are
incorporated in a flexible way.  There are many ways to do this, I've
shown one, here's another:

f <- function(lambda) -sum(dpois(x, lambda, log=T))
d <- data.frame(x=rpois(10000, 12.34))
environment(f)<-evalq(environment(),d)
mle(f, start=list(lambda=10))

Call:
mle(minuslogl = f, start = list(lambda = 10))

Coefficients:
 lambda
12.3402

set.seed(1)
library(proto)
f <- function(lambda) -sum(dpois(x, lambda, log=TRUE))
p <- proto(f = f, x = rpois(100, 12.34))
mle(p[["f"]], start = list(lambda = 10))

It is not at all an unlikely design to have mle() as a generic function
which works on many kinds of objects, the default method being
function(object,...) mle(minuslogl(obj)) and minuslogl is an extractor
function returning (tada!) the negative log likelihood function.

  (My version also has a cool formula interface and other
bells and whistles, and I would love to get feedback from other
useRs about it.)

   cheers
    Ben Bolker

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  O__  ---- Peter Dalgaard             ?ster Farimagsgade 5, Entr.B
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