Harold,
I have tried the following syntax:
> fm <- lmer(RATING ~ CHAIN*SECTOR*RESP +(1|CHAIN*SECTOR*RESP), gt)
> summary(fm)
Linear mixed-effects model fit by REML
Formula: RATING ~ CHAIN * SECTOR * RESP + (1 | CHAIN * SECTOR * RESP)
Data: gt
AIC BIC logLik MLdeviance REMLdeviance
2767.466 2807.717 -1374.733 2710.253 2749.466
Random effects:
Groups Name Variance Std.Dev.
CHAIN * SECTOR * RESP (Intercept) 5.7119 2.3900
Residual 2.8247 1.6807
number of obs: 647, groups: CHAIN * SECTOR * RESP, 71
Fixed effects:
Estimate Std. Error t value
(Intercept) 4.5760000 2.6193950 1.74697
CHAIN -0.2014603 0.7984752 -0.25231
SECTOR -0.1093434 2.3516722 -0.04650
RESP 0.0184237 0.0276326 0.66674
CHAIN:SECTOR 0.1423668 0.3005919 0.47362
CHAIN:RESP 0.0024786 0.0083782 0.29584
SECTOR:RESP -0.0046001 0.0240517 -0.19126
CHAIN:SECTOR:RESP -0.0011219 0.0030762 -0.36470
Correlation of Fixed Effects:
(Intr) CHAIN SECTOR RESP CHAIN:SECTOR CHAIN:R SECTOR:
CHAIN -0.435
SECTOR -0.845 -0.050
RESP -0.778 0.345 0.645
CHAIN:SECTOR 0.886 -0.732 -0.635 -0.680
CHAIN:RESP 0.351 -0.782 0.038 -0.466 0.566
SECTOR:RESP 0.666 0.038 -0.786 -0.822 0.500 -0.046
CHAIN:SECTOR: -0.709 0.586 0.500 0.879 -0.789 -0.729 -0.635
>
Again, my problem is: there are no fixed effects...
The same dataset, when running at SPSS (I have a subset with 647
records), using the syntax I showed somewhere before, gives me the
following output:
Variance Components Estimation
Variance Estimates
Component Estimate
Var(CHAIN) ,530
Var(SECTOR) ,000(a)
Var(RESP) 2,734
Var(ASPECT) ,788
Var(ITEM) ,000(a)
Var(SECTOR * ,061
RESP)
Var(SECTOR * ,000(a)
ASPECT)
Var(SECTOR * ,031
ITEM)
Var(CHAIN * 2,183
RESP)
Var(CHAIN * ,038
ASPECT)
Var(CHAIN * ,003
ITEM)
Var(RESP * ,467
ASPECT)
Var(RESP * ,279
ITEM)
Var(SECTOR * ,000(a)
RESP * ASPECT)
Var(SECTOR * ,077
RESP * ITEM)
Var(CHAIN * ,773
RESP * ASPECT)
Var(Error) ,882
Dependent Variable: RATING
Method: Restricted Maximum Likelihood Estimation
a This estimate is set to zero because it is redundant.
That's what I would like to get from R.
Any help would be appreciated.
Best regards,
Iuri
On 8/20/06, Iuri Gavronski <[EMAIL PROTECTED]> wrote:
>
> 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 <[EMAIL PROTECTED]> 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: [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.
> > > > >
> > > >
> > >
> > > [[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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