[R] Fwd: Variance Components in R
Spencer Graves
spencer.graves at pdf.com
Thu Aug 17 18:06:17 CEST 2006
Hi, Iuri:
How much RAM and how fast a microprocessor (and what version of
Windows) do you have? You might still try it in R under Windows. The
results might be comparable or dramatically better in R than in SPSS or
SAS.
hope this helps.
Spencer Graves
Iuri Gavronski wrote:
> 9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am
> trying to convert the SPSS script to R to run in a RISC station at the
> university.
>
> On 8/17/06, Doran, Harold <HDoran at air.org> wrote:
>
>> Iuri:
>>
>> The lmer function is optimal for large data with crossed random effects.
>> How large are your data?
>>
>>
>>> -----Original Message-----
>>> From: r-help-bounces at stat.math.ethz.ch
>>> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Iuri Gavronski
>>> Sent: Thursday, August 17, 2006 11:08 AM
>>> To: Spencer Graves
>>> Cc: r-help at stat.math.ethz.ch
>>> Subject: Re: [R] Variance Components in R
>>>
>>> Thank you for your reply.
>>> VARCOMP is available at SPSS advanced models, I'm not sure
>>> for how long it exists... I only work with SPSS for the last
>>> 4 years...
>>> My model only has crossed random effects, what perhaps would
>>> drive me to lmer().
>>> However, as I have unbalanced data (why it is normally called
>>> 'unbalanced design'? the data was not intended to be
>>> unbalanced, only I could not get responses for all cells...),
>>> I'm afraid that REML would take too much CPU, memory and time
>>> to execute, and MINQUE would be faster and provide similar
>>> variance estimates (please, correct me if I'm wrong on that point).
>>> I only found MINQUE on the maanova package, but as my study
>>> is very far from genetics, I'm not sure I can use this package.
>>> Any comment would be appreciated.
>>> Iuri
>>>
>>> On 8/16/06, Spencer Graves <spencer.graves at pdf.com> wrote:
>>>
>>>> I used SPSS over 25 years ago, but I don't recall
>>>>
>>> ever fitting a
>>>
>>>> variance components model with it. Are all your random
>>>>
>>> effects nested?
>>>
>>>> If they were, I would recommend you use 'lme' in the 'nlme' package.
>>>> However, if you have crossed random effects, I suggest you
>>>>
>>> try 'lmer'
>>>
>>>> associated with the 'lme4' package.
>>>>
>>>> For 'lmer', documentation is available in Douglas
>>>>
>>> Bates. Fitting
>>>
>>>> linear mixed models in R. /R News/, 5(1):27-30, May 2005
>>>> (www.r-project.org -> newsletter). I also recommend you try the
>>>> vignette available with the 'mlmRev' package (see, e.g.,
>>>> http://finzi.psych.upenn.edu/R/Rhelp02a/archive/81375.html).
>>>>
>>>> Excellent documentation for both 'lme' (and indirectly for
>>>> 'lmer') is available in Pinheiro and Bates (2000)
>>>>
>>> Mixed-Effects Models
>>>
>>>> in S and S-Plus (Springer). I have personally recommended
>>>>
>>> this book
>>>
>>>> so many times on this listserve that I just now got 234 hits for
>>>> RSiteSearch("graves pinheiro"). Please don't hesitate to pass this
>>>> recommendation to your university library. This book is
>>>>
>>> the primary
>>>
>>>> documentation for the 'nlme' package, which is part of the
>>>>
>>> standard R
>>>
>>>> distribution. A subdirectory "~library\nlme\scripts" of your R
>>>> installation includes files named "ch01.R", "ch02.R", ...,
>>>>
>>> "ch06.R",
>>>
>>>> "ch08.R", containing the R scripts described in the book. These R
>>>> script files make it much easier and more enjoyable to study that
>>>> book, because they make it much easier to try the commands
>>>>
>>> described
>>>
>>>> in the book, one line at a time, testing modifications to check you
>>>> comprehension, etc. In addition to avoiding problems with
>>>> typographical errors, it also automatically overcomes a few
>>>>
>>> minor but
>>>
>>>> substantive changes in the notation between S-Plus and R.
>>>>
>>>> Also, the "MINQUE" method has been obsolete for over
>>>>
>>> 25 years.
>>>
>>>> I recommend you use method = "REML" except for when you want to
>>>> compare two nested models with different fixed effects; in
>>>>
>>> that case,
>>>
>>>> you should use method = "ML", as explained in Pinheiro and
>>>>
>>> Bates (2000).
>>>
>>>> Hope this helps.
>>>> Spencer Graves
>>>>
>>>> Iuri Gavronski wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I'm trying to fit a model using variance components in R, but if
>>>>> very new on it, so I'm asking for your help.
>>>>>
>>>>> I have imported the SPSS database onto R, but I don't know how to
>>>>> convert the commands... the SPSS commands I'm trying to
>>>>>
>>> convert are:
>>>
>>>>> VARCOMP
>>>>> RATING BY CHAIN SECTOR RESP ASPECT ITEM
>>>>> /RANDOM = CHAIN SECTOR RESP ASPECT ITEM
>>>>> /METHOD = MINQUE (1)
>>>>> /DESIGN = CHAIN SECTOR RESP ASPECT ITEM
>>>>> SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP
>>>>> CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM
>>>>> SECTOR*RESP*ASPECT SECTOR*RESP*ITEM
>>>>>
>>> CHAIN*RESP*ASPECT
>>>
>>>>> /INTERCEPT = INCLUDE.
>>>>>
>>>>> VARCOMP
>>>>> RATING BY CHAIN SECTOR RESP ASPECT ITEM
>>>>> /RANDOM = CHAIN SECTOR RESP ASPECT ITEM
>>>>> /METHOD = REML
>>>>> /DESIGN = CHAIN SECTOR RESP ASPECT ITEM
>>>>> SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP
>>>>> CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM
>>>>> SECTOR*RESP*ASPECT SECTOR*RESP*ITEM
>>>>>
>>> CHAIN*RESP*ASPECT
>>>
>>>>> /INTERCEPT = INCLUDE.
>>>>>
>>>>> Thank you for your help.
>>>>>
>>>>> Best regards,
>>>>>
>>>>> Iuri.
>>>>>
>>>>> _______________________________________
>>>>> Iuri Gavronski - iuri at ufrgs.br
>>>>> doutorando
>>>>> UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil
>>>>>
>>>>> ______________________________________________
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>>>>
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