On Aug 4, 2009, at 7:48 PM, Hongwei Dong wrote:
Yeah, I have a very large sample size, about 60,000 observations.
Multicollinearity should not be a problem here. The weird thing is
that SPSS
can converge very quickly and gives out reasonable results.
The only problem I can think of is that, my first level (random)
variables
are dummy variables: 6 housing types, and I used five dummies in
model and
one as the reference. I also tried to combine them into two groups
and use
only dummy at random level, but it does not work either.
is there any one here has similar experience with the LME function
in R?
I have absolutely no experience with "LME" but I can predict with very
high probability that you would be getting more sensible result if you
modeled those housing types with a single factor variable rather than
creating 6 dummies. ((Would one generally not create a reference dummy?)
?factor
--
David.
Thanks.
Harry
On Tue, Aug 4, 2009 at 1:28 AM, ONKELINX, Thierry
<thierry.onkel...@inbo.be>wrote:
Dear Harry,
Your model seems rather complex. Do you have enough data to support
it?
Did you check for multicollinearity between the variables?
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for
Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no
more
than asking him to perform a post-mortem examination: he may be
able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer
does not
ensure that a reasonable answer can be extracted from a given body of
data.
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-----Oorspronkelijk bericht-----
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org
]
Namens Hongwei Dong
Verzonden: maandag 3 augustus 2009 19:45
Aan: r-help@r-project.org
Onderwerp: Re: [R] lme funcion in R
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+p
ark_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-h...@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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