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Iuri Gavronski

2006-Aug-10 19:29 UTC

[R] Variance Components in R

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 at ufrgs.br
doutorando
UFRGS/PPGA/NITEC - www.ppga.ufrgs.br
Brazil

Spencer Graves

2006-Aug-17 00:34 UTC

head link

[R] Variance Components in R

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 at ufrgs.br
> doutorando
> UFRGS/PPGA/NITEC - www.ppga.ufrgs.br
> Brazil
>
> ______________________________________________
> R-help at 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.
>

Doran, Harold

2006-Aug-17 15:08 UTC

head link

[R] Variance Components in R

Iuri:

The lmer function is optimal for large data with crossed random effects.
How large are your data?
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Iuri Gavronski
> Sent: Thursday, August 17, 2006 11:08 AM
> To: Spencer Graves
> Cc: r-help at 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 at 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 at ufrgs.br
> > > doutorando
> > > UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil
> > >
> > > ______________________________________________
> > > R-help at 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 at 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.
>

Doran, Harold

2006-Aug-17 15:19 UTC

head link

[R] Variance Components in R

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.
		> > >
		> >
		>
		>       [[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]]

Iuri Gavronski

2006-Aug-17 15:23 UTC

head link

[R] Fwd: 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
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Doran, Harold

2006-Aug-17 21:33 UTC

head link

[R] Variance Components in R

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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			>
			> ______________________________________________
			> 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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