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 >> >> > > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
