Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20101026/a2100eb8/attachment.pl>
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
On 10/26/2010 1:38 PM, justin bem wrote:> Dear all,
>
> By default the glm function in the stats package use IWLS. How can I fit a
glm
> model using BFGS algorithm ?
> Justin BEM
> BP 1917 Yaound?
> T?l (237) 76043774
>
>
>
>
> [[alternative HTML version deleted]]
>
>
>
>
> ______________________________________________
> R-help at 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.
--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
Web: http://www.erasmusmc.nl/biostatistiek/
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.rizopoulos at erasmusmc.nl> > To: justin bem <justin_bem at yahoo.fr> > Cc: R Maillist <r-help at stat.math.ethz.ch> > Subject: Re: [R] Fitting GLM with BFGS algorithm > Message-ID: <4CC6C0DB.9070800 at 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