Dear mixed modelers,

When lme4 fits a glmer model, the default parameterization is to model the
full covariance matrix of random effects.  For some data sets, this takes a
very long time, and the covariance is also difficult to estimate with
missing data.  Is there a way to set the correlation matrix to 0?  [I can
think of ways to hack it, but I wonder if there is a command I haven't
noticed.]

To make my question more concrete, consider an example where I am modeling
counts of animals at several sites through time:
glmer(count ~ -1 + site + (-1 + site|year), family = poisson)

This model estimates the average log-count of animals at each site, the
standard deviation through time at each site, and the covariance of
log-counts at all pairs of sites through time.  [The -1's set the
parameterization to means and standard deviations for each site, as opposed
to a reference group, and deviations from that group.] Missing data are
sites with no counts in some years, which is one thing that makes the
covariance difficult to estimate.

Is there a command to set the random effects correlations to 0, without
manually creating a dummy variable for each site, running a separate lmer
model for each site?

Thanks!
Elizabeth Crone


-- 

****************************
Elizabeth E. Crone
Associate Professor, Department of Biology
Tufts University
163 Packard Avenue
Medford MA 02155
USA

email: elizabeth.crone@tufts.edu
phone: 617-627-0847
fax: 617-627-3805
office/lab: Barnum 016

	[[alternative HTML version deleted]]

