From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Fri 18 Aug 2006 - 01:59:29 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Fri Aug 18 02:07:09 2006

Date: Fri 18 Aug 2006 - 01:59:29 EST

Hi, Iuri:

If you've got an 8086 AND a huge data set, compute time might be a problem with 'lmer'. However, if you a reasonably modern computer and only a a few thousand observations, 'lmer' should complete almost in the blink of an eye -- or at least in less time than it would talk for a cup of coffee.

Spencer

Doran, Harold wrote:

> This will (should) be a piece of cake for lmer. But, I don't speak SPSS.

*> Can you write your model out as a linear model and give a brief
**> description of the data and your problem?
**>
**> In addition to what Spencer noted as help below, you should also check
**> out the vignette in the mlmRev package. This will give you many
**> examples.
**>
**> vignette('MlmSoftRev')
**>
**>
**>
**>
**>
**> ________________________________
**>
**> From: prof.iuri@gmail.com [mailto:prof.iuri@gmail.com] On Behalf
**> Of Iuri Gavronski
**> Sent: Thursday, August 17, 2006 11:16 AM
**> To: Doran, Harold
**> Subject: Re: [R] Variance Components in R
**>
**>
**> 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@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@stat.math.ethz.ch
**> > [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of
**> Iuri Gavronski
**> > Sent: Thursday, August 17, 2006 11:08 AM
**> > To: Spencer Graves
**> > Cc: r-help@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@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@ufrgs.br
**> > > > doutorando
**> > > > UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil
**> > > >
**> > > > ______________________________________________
**> > > > R-help@stat.math.ethz.ch mailing list
**> > > > https://stat.ethz.ch/mailman/listinfo/r-help
**> > > > PLEASE do read the posting guide
**> > > http://www.R-project.org/posting-guide.html
**> > > > and provide commented, minimal, self-contained,
**> reproducible code.
**> > > >
**> > >
**> >
**> > [[alternative HTML version deleted]]
**> >
**> > ______________________________________________
**> > R-help@stat.math.ethz.ch mailing list
**> > https://stat.ethz.ch/mailman/listinfo/r-help
**> > PLEASE do read the posting guide
**> > http://www.R-project.org/posting-guide.html
**> > and provide commented, minimal, self-contained,
**> reproducible code.
**> >
**>
**>
**>
**>
**> [[alternative HTML version deleted]]
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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**>
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