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. > > > > > [[alternative HTML version deleted]]