[R] LR-based CIs for GLMs

[Prof Brian D Ripley]

On Wed, 21 Mar 2001, Tim CHURCHES wrote:

> We are using glm() to models to counts of deaths due to rare causes
using a log link and Poisson error distribution, with population as
the offset. Approximate confidence intervals for the parameter estimates
are easy to calculate using a standard normal deviate, but obviously when
the counts of deaths are small (which is why we are using Poisson regression),
these intervals are very approximate indeed.

That's not so obvious: you may have many small counts and get a good
nomral approximation.

> Has anyone done any work on calculating  more precise likelihood ratio
- based confidence intervals for parameter estimates from generalised
linear models? These are also known as "profile likelihood confidence
intervals", I believe. PROC GENMOD in SAS can calculate them -
I am happy to email the relevant page from the SAS online documentation
which describes the way in which they are calculated in more detail to
anyone who is interested - but we would much rather use R for this work...

Look at confint in package MASS, in the VR bundle.  It has a method
for glm fits.  Make sure you use the latest version, as some have had
problems with that function on R.

BTW, please wrap your lines when posting: it makes the responder's task
easier and the structure of the response easier too.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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