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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 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 <[EMAIL PROTECTED]> 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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 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 <[EMAIL PROTECTED]> wrote:
Iuri:
The lmer function is optimal for large data with
crossed random effects.
How large are your data?
> -----Original Message-----
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] 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 <[EMAIL PROTECTED]> 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 - [EMAIL PROTECTED]
> > > 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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