Thanks for the replies above. Here are my script and data structure:
library(nlme)
tlevel<-lme(fixed = LN_unitlandval ~
MH_D+APT_D+ResOth_D+NonRes_D+Vacant_D+access_emp1+pct_vacant+transit_D+park_dum,data=lusdrdata,random
= ~MH_D+APT_D+ResOth_D+NonRes_D+Vacant_D | TAZ)

str:

$ TAZ : int 100 100 100 100 100 100 100 100 100 100 ...
$ MH_D : num 0 0 0 0 0 0 0 0 0 0 ...
$ APT_D : num 0 0 0 0 0 0 0 0 0 0 ... $ ResOth_D : num 0 0 0 0 0 0 0 0 0 0
... $ NonRes_D : num 0 0 0 0 0 0 0 0 0 1 ...
$ Vacant_D : num 1 1 1 0 0 1 1 1 1 0 ...
$ access_emp1 : num 45.8 45.8 45.8 45.8 45.8 ...
$ pct_vacant : num 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ... $ transit_D :
num 0 0 0 0 0 0 0 0 0 0 ... $ park_dum : num 0 0 0 0 0 0 0 0 0 0 ...


Thanks.

Harry



On Mon, Aug 3, 2009 at 10:36 AM, Jason Morgan <jwm-r-help@skepsi.net> wrote:

> On 2009.08.03 10:15:46, Hongwei Dong wrote:
> > Hi, R users,
> >   I'm using the "lme" function in R to estimate a 2 level mixed effects
> > model, in which the size of the subject groups are different. It turned
> out
> > that It takes forever for R to converge. I also tried the same thing in
> SPSS
> > and SPSS can give the results out within 20 minutes. Anyone can give me
> some
> > advice on the lme function in R, especially why R does not converge?
> Thanks.
> >
> > Harry
>
> Hello Harry,
>
> As Chuck mentions, providing some more information on the model and
> the data you are using would be helpful. Also, be sure to compare the
> optimization methods used in SPSS to that used in R. You can change
> the optimization method in R if the default seems to be causing
> issues. See help(lmeControl) for numerous setting options.
>
> ~Jason
>
> --
> Jason W. Morgan
> Graduate Student
> Department of Political Science
> *The Ohio State University*
> 154 North Oval Mall
> Columbus, Ohio 43210
>
>

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