On Wed, 18 Feb 2009, jjh21 wrote:
Hello,
I know that two possible approaches to dealing with clustered data would be
GEE or a robust cluster covariance matrix from a standard regression. What
are the differences between these two methods, or are they doing the same
thing? Thanks.
There are two components to 'GEE'. The first is the robust cluster (or
'sandwich') covariance, the second is the ability to choose a weight matrix to
get higher efficiency ('working correlation').
Using the 'independence working correlation' asks for the same weighting as in
ordinary regression, so the estimates are the same as in standard regression,
and then the standard errors are the same as the 'robust cluster' ones (up to
factors of n/(n-1) and similar implementation details). The standard errors are
also the same as the Horvitz-Thompson estimator gives for cluster sampling from
an infinite population, and they are also the same as an approximation to the
cluster jackknife standard errors where a cluster is downweighted slightly
rather than removed.
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
______________________________________________
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.
