Re: [R] Variance Components in R

From: Doran, Harold <HDoran_at_air.org>
Date: Fri 18 Aug 2006 - 07:33:20 EST


Iuri:  

Here is an example of how a model would be specified using lmer using a couple of your factors:  

fm <- lmer(response.variable ~ chain*sector*resp +(chain*sector*resp|GroupingID), data)  

This will give you a main effect for each factor and all possible interactions. However, do you have a grouping variable? I wonder if aov might be the better tool for your G-study?  

As a side note, I don't see that you have a factor for persons. Isn't this also a variance component of interest for your study?


	From: prof.iuri@gmail.com [mailto:prof.iuri@gmail.com] On Behalf Of Iuri Gavronski
	Sent: Thursday, August 17, 2006 1:26 PM
	To: Doran, Harold
	Cc: r-help@stat.math.ethz.ch
	Subject: Re: [R] Variance Components in R
	
	
	I am trying to replicate Finn and Kayandé (1997) study on G-theory application on Marketing. The idea is to have people evaluate some aspects of service quality for chains on different economy sectors. Then, conduct a G-study to identify the generalizability coefficient estimates for different D-study designs. 
	I have persons rating 3 different items on 3 different aspects of service quality on 3 chains on 3 sectors. It is normally assumed on G-studies that the factors are random. So I have to specify a model to estimate the variance components of CHAIN SECTOR RESP ASPECT ITEM, and the interaction of 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. '*' in VARCOMP means a crossed design.
	Evaluating only the two dimensions interactions (x*y) ran in few minutes with the full database. Including three interactions (x*y*z) didn't complete the execution at all. I have the data and script sent to a professor of the department of Statistics on my university and he could not run it on either SPSS or SAS (we don't have SAS licenses here at the business school, only SPSS). Nobody here at the business school has any experience with R, so I don't have anyone to ask for help. 
	Ì am not sure if I have answered you question, but feel free to ask it again, and I will try to restate the problem.
	
	Best regards,
	
	Iuri
	
	
	On 8/17/06, Doran, Harold <HDoran@air.org> 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.
> > >
> >
>
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