Dear Professor Bates,

Thanks, that will probably do the job. 

By the way, how to cite lme4 in my work? 

Shige

On 8/31/05, Douglas Bates <dmbates@gmail.com> wrote:
> 
> On 8/30/05, Shige Song <shigesong@gmail.com> wrote:
> > Dear All,
> >
> > Can anyone give me some hints about how to set starting values for a 
> lmer
> > model? For complicated models like this, good starting values can help 
> the
> > numerical computation and make the model converge faster. Thanks!
> >
> > Shige
> 
> I agree but I haven't gotten around to designing how that could be
> done. It could be easy or difficult depending on how you want to
> represent the starting values.
> 
> If you look at the (only) lmer method function you will see that it
> has a section
> 
> if (lmm) { ## linear mixed model
> .Call("lmer_initial", mer, PACKAGE="Matrix")
> .Call("lmer_ECMEsteps", mer, cv$niterEM, cv$EMverbose,
> PACKAGE = "Matrix")
> LMEoptimize(mer) <- cv
> 
> for linear mixed models. The object "mer" is a mixed-effects
> representation and the list "cv" is the control values. The only
> thing that the C function "lmer_initial" does is set the initial
> values of the relative precision matrices for the random effects.
> These are the inverses of the variance-covariance matrices relative to
> the variance of the per-observation noise term. They are stored
> (upper triangle only) in a slot called "Omega" of the mer class (which
> is contained in the lmer class).
> 
> There is no purpose in setting initial values for the fixed-effects
> parameters or the variance of the per-observation noise term because
> these are profiled out of the optimization. The optimization is only
> over the values in the Omega slot.
> 
> I can allow those values to be set from an argument and only call
> "lmer_initialize" if that argument is missing. Will that be
> sufficient for you?
>

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