suggested modification to the 'mle' documentation?
On 12/7/2007 8:10 AM, Peter Dalgaard wrote:
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.
We should allow more general things to be passed as data arguments in cases where it makes sense. For example a list with names or an environment would be a reasonable way to pass data that doesn't fit into a data frame.
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)
We really need to expand as.environment, so that it can convert data frames into environments. You should be able to say: environment(f) <- as.environment(d) and get the same result as environment(f)<-evalq(environment(),d) But I'd prefer to avoid the necessity for users to manipulate the environment of a function. I think the pattern model( f, data=d ) being implemented internally as environment(f) <- as.environment(d, parent = environment(f)) is very nice and general. It makes things like cross-validation, bootstrapping, etc. conceptually cleaner: keep the same formula/function f, but manipulate the data and see what happens. It does have problems when d is an environment that already has a parent, but I think a reasonable meaning in that case would be to copy its contents into a new environment with the new parent set. Duncan Murdoch
mle(f, start=list(lambda=10))
Call: mle(minuslogl = f, start = list(lambda = 10)) Coefficients: lambda 12.3402 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