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 x...@utep.edu xiaogan...@gmail.com https://sites.google.com/site/xgsu00/ [[alternative HTML version deleted]] ______________________________________________ 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.