Dear Professor Bates,
Thank you very much for the help. I cannot waiting to see your new book!
Best,
Shige
On 9/6/05, Douglas Bates <[EMAIL PROTECTED]> wrote:
>
> On 9/5/05, Shige Song <[EMAIL PROTECTED]> wrote:
> > Dear Professor Bates,
> >
> > Thanks, that will probably do the job.
>
> OK, I will add that capability.
>
> > By the way, how to cite lme4 in my work?
>
> For now I suggest citing either the package itself or the R News
> article that I wrote about it.
>
> @Article{Rnews:Bates:2005,
> author = {Douglas Bates},
> title = {Fitting Linear Mixed Models in {R}},
> journal = {R News},
> year = 2005,
> volume = 5,
> number = 1,
> pages = {27--30},
> month = {May},
> url = {http://CRAN.R-project.org/doc/Rnews/},
> }
>
> Eventually there will be a book that you can cite but I have to finish
> writing it first :-)
>
> >
> > Shige
> >
> > On 8/31/05, Douglas Bates <[EMAIL PROTECTED]> wrote:
> > >
> > > On 8/30/05, Shige Song <[EMAIL PROTECTED]> 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?
> > >
> >
> > [[alternative HTML version deleted]]
> >
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> > PLEASE do read the posting guide!
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> >
>
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