Harold, I have tried to adapt your syntax and got some problems. Some
responses from lmer:
On this one, I have tried to use "1" as a grouping variable. As I understood
from Bates (2005), grouping variables are like nested design, which is not
the case.
> fm <- lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR*RESP|1), gt)
Erro em lmer(RATING ~ CHAIN * SECTOR * RESP + (CHAIN * SECTOR * RESP | :
Ztl[[1]] must have 1 columns
Then I have tried to ommit the fixed effects...
> fm <- lmer(RATING ~ (CHAIN*SECTOR*RESP|1), gt)
Erro em x[[3]] : não é possível dividir o objeto em subconjuntos
(the error message would be something like "not possible to divide the
object in subsets"... I don't know the original wording of message because
my R is in Portuguese...)
Then... I have tried to specify RESP (the persons) as the grouping variable
(which doesn't make any sense to me, but...)
> fm <- lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR|RESP), gt)
Warning message:
nlminb returned message false convergence (8)
in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, tolerance =
1.49011611938477e-08,
>
Any idea?
Regards,
Iuri.
On 8/17/06, Doran, Harold wrote:
>
> 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 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 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 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.
> >
>
>
>
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