st: Estimation metric of variance components in gllamm

[Sascha Peter <sascha.peter@uni-hamburg.de>]

Dear Stata-Users,
I have a question regarding the estimation metric of random effects  
covariances in gllamm.
I want to use estimation results from Stata's xtmepoisson as starting  
values to estimate a multilevel Poisson regression in gllamm. Since  
the estimation metric for the variance components of the two programs  
differ, variances and covariances stored in e(b) after xtmepoisson  
have to be transformed before these can be used as starting values for  
gllamm.
xtmepoisson uses the log of the standard deviation as estimation  
metric and gllamm uses the standard deviation. So exponentiation of  
the respective elements in e(b) will do the job.
xtmepoisson uses the arc-hyperbolic tangents of correlations to  
estimate covariance components. However,I was not able to figure out  
in which metric these are estimated in gllamm. Which transformation  
has to be applied to the covariance components in e(b) so that these  
estimates can be used as starting values in gllamm?

Thank you very much,
Sascha

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