In the sort of problem mentioned below, the suggestion to put in gradients (I believe this is what is meant by "minus score vector") is very important. Using analytic gradients is almost always a good idea in optimization of smooth functions for both efficiency of computation and quality of results.
Also users may want to use either updated codes (Rvmmin is BFGS algorithm with box constraints; ucminf does it unconstrained) or different approaches, depending on the function. Package optimx lets users discover relative properties of different optimizers on their class of problems. John Nash > From: Dimitris Rizopoulos <d.rizopou...@erasmusmc.nl> > To: justin bem <justin_...@yahoo.fr> > Cc: R Maillist <r-h...@stat.math.ethz.ch> > Subject: Re: [R] Fitting GLM with BFGS algorithm > Message-ID: <4cc6c0db.9070...@erasmusmc.nl> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > for instance, for logistic regression you can do something like this: > > # simulate some data > x <- cbind(1, runif(100, -3, 3), rbinom(100, 1, 0.5)) > y <- rbinom(100, 1, plogis(c( x%*% c(-2, 1, 0.3)))) > > # BFGS from optim() > fn <- function (betas, y, x) { > -sum(dbinom(y, 1, plogis(c(x %*% betas)), log = TRUE)) > } > optim(rep(0, ncol(x)), fn, x = x, y = y, method = "BFGS") > > # IWLS from glm() > glm(y ~ x[, -1], family = "binomial") > > You can also improve it by providing the minus score vector as a third > argument to optim(). > > > I hope it helps. > > Best, > Dimitris ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.