look at summary.glm(), probably you're looking for fit <- glm(..., family = binomial) # the inverse Fisher Information matrix summary(fit)$cov.scaled
I hope it helps. Best, Dimitris -- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/(0)16/336899 Fax: +32/(0)16/337015 Web: http://med.kuleuven.be/biostat/ http://www.student.kuleuven.be/~m0390867/dimitris.htm Quoting "Bickel, David" <[EMAIL PROTECTED]>: > Is there a function that provides the Fisher information matrix for a > generalized linear model? I do not see how to access the off-diagonal > matrix elements of the value returned by glm. (I'm particularly > interested in logistic regression.) > > If not, what is a good way to use R to compute Hessians or other partial > derivatives of log likelihoods? > > I would appreciate any guidance. > > David > _______________________________________ > David R. Bickel http://davidbickel.com > Research Scientist > Pioneer Hi-Bred International (DuPont) > Bioinformatics > 7200 NW 62nd Ave.; PO Box 184 > Johnston, IA 50131-0184 > 515-334-4739 Tel > 515-334-4473 Fax > [EMAIL PROTECTED] > > This communication is for use by the intended recipient and ...{{dropped}} > > ______________________________________________ > [email protected] 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. > > Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm ______________________________________________ [email protected] 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.
