Dear All,

Does anyone know if there is any way to obtain the variance-covariance
matrix for any arbitrarily given estimates with the glm() function?

Here is what I really want. Given an arbitrary estimate (i.e., as starting
points with the start= argument), the glm() function could return the
corresponding variance-covariance matrix (or Hessian) and other quantities
with no Netwon-Raphson iteration? This could have been done by setting
maxit=0, but very unfortunately that is not an option in glm.control(). To
illustrate the problem,

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
beta0 <- 1:3
control0 <- glm.control(epsilon = 1e10, maxit = 0, trace = FALSE)
fit <- glm(admit ~ gre + gpa, data = mydata, family = "binomial",
start=beta0, control=control0)
summary(fit)$"cov.scaled"

By the way, I am aware that I could directly compute the covariance matrix
using the formula. I also know that I could extract the corresponding
deviance by using the offset option.

Any suggestion is greatly appreicated.

Thanks,
Xiaogang Su

=============================
Xiaogang Su, Ph.D.
Associate Professor
Department of Mathematical Sciences
University of Texas at El Paso
500 W. University Ave.
El Paso, Texas 79968-0514
xsu@utep.edu
xiaogangsu@gmail.com
https://sites.google.com/site/xgsu00/

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