suggested modification to the 'mle' documentation?

On Fri, 7 Dec 2007, Duncan Murdoch wrote:



For working at the general likelihood I think is is better to
encourage the approach of definign likelihood constructor functions.
The problem with using f, data is that you need to mathc the names
used in f and in data, so either you have to explicitly write out f
with the names you have in data or you have to modify data to use the
names f likes -- in the running example think

    f <- function(lambda) -sum(dpois(x, lambda, log=T))
    d <- data.frame(y=rpois(10000, 12.34))

somebody has to connext up the x in f with the y in d. With a negative
log likelihood constructor defines, for example, as

    makePoisonNegLogLikelihood <- function(x)
        function(lambda) -sum(dpois(x, lambda, log=T))

this happens naturally with

    makePoisonNegLogLikelihood(d$y)

Both (simple) bootstrapping and (simple leave-one-out) crossvalidation
require a data structure with a notion of cases, which is much more
restrictive than the conext in which mle can be used.  A more ngeneric
aproach to bootstrapping that might fit closer to the level of
generality of mle might be parameterized in terms of a negative log
likelihood constructor, a starting value constructor, and a resampling
function, with a single iteration implemented soemthing like

    mleboot1 <- function(nllmaker, start, esample)  {
    newdata <- resample()
    newstart <- do.call(start, newdata)
    nllfun <- do.call(nllmaker, newdata)
    mle(fnllfun, start = newstart)
    }

This would leave decisions on the resampling method and data structure
up to the user. Somehing similar could be done with K-fold CV.

luke