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 Yaoundé
Tél (237) 76043774
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
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