In addition, it seems the lme function in R are very troubled with the dummy
variables used at the first (random) level.
Harry


On Tue, Aug 4, 2009 at 6:06 PM, Hongwei Dong <pdxdong@gmail.com> wrote:

> Thanks, David, you are right. If I use continuous data such as 1, 2, ...6
> to represent those 6 housing types, the model works with the lme function in
> R. The problem is, the relationship between the 6 housing types are not
> continuous, which we assume when we use 1,2,..6 to represent them.
> Harry
>
>
>
> On Tue, Aug 4, 2009 at 5:47 PM, David Winsemius <dwinsemius@comcast.net>wrote:
>
>>
>> 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.ONKELINX@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.Onkelinx@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.
>>>> ~ John Tukey
>>>>
>>>> -----Oorspronkelijk bericht-----
>>>> Van: r-help-bounces@r-project.org [mailto:r-help-bounces@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-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
>>>>>
>>>>>
>>>>>
>>>>       [[alternative HTML version deleted]]
>>>>
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>>>        [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
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>>>
>>
>> David Winsemius, MD
>> Heritage Laboratories
>> West Hartford, CT
>>
>>
>

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