Re: [R] Variance Components in R

From: Doran, Harold <HDoran_at_air.org>
Date: Fri 18 Aug 2006 - 01:19:42 EST


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.

> > >
> >
>
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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 01:28:52 2006

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