[R] structured random effects

2004-02-06 Thread David Firth 
Are there facilities in R or packages to estimate the parameters of a 
(generalized) linear mixed model like this one: the design is crossed, 
and response $y_{ij}$ relates to fixed and random effects through a 
linear predictor
\[
  \eta_{ij} = \beta x_{ij} + U_i - U_j
\]
where $U_1, U_2, \ldots$ are iid $N(0, \tau^2)$.

Any suggestions would be welcomed.

David

Professor David Firth
Dept of Statistics
University of Warwick
Coventry CV4 7AL
United Kingdom
Voice: +44 (0)247 657 2581
Fax:   +44 (0)247 652 4532
Web:   http://www.warwick.ac.uk/go/dfirth
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Re: [R] structured random effects

2004-02-06 Thread Spencer Graves 
 Have you considered lme?  For applications like this, I highly 
recommend Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus 
(Springer).  I failed to produce anything with lme until I read this 
book. 

 hope this helps.  spencer graves

David Firth wrote:

Are there facilities in R or packages to estimate the parameters of a 
(generalized) linear mixed model like this one: the design is crossed, 
and response $y_{ij}$ relates to fixed and random effects through a 
linear predictor
\[
  \eta_{ij} = \beta x_{ij} + U_i - U_j
\]
where $U_1, U_2, \ldots$ are iid $N(0, \tau^2)$.

Any suggestions would be welcomed.

David

Professor David Firth
Dept of Statistics
University of Warwick
Coventry CV4 7AL
United Kingdom
Voice: +44 (0)247 657 2581
Fax:   +44 (0)247 652 4532
Web:   http://www.warwick.ac.uk/go/dfirth
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Re: [R] structured random effects

2004-02-06 Thread David Firth 
Thanks to Douglas and Spencer for their helpful replies.

I take that as a fairly authoritative no to my question!  The 
difficulty
in the model I mentioned is not only the crossed design, but the
homologous factors (ie i and j take the same values),
and U_i - U_j with the *same* U variable
appearing twice with different subscripts in the predictor.

Thanks again -- I am happy to know that If I work on this I'm not
reinventing what already exists.
Best regards,
David
On Friday, Feb 6, 2004, at 18:28 Europe/London, Douglas Bates wrote:

Spencer Graves [EMAIL PROTECTED] writes:

  Have you considered lme?  For applications like this, I highly
recommend Pinheiro and Bates (2000) Mixed-Effects Models in S and
S-Plus (Springer).  I failed to produce anything with lme until I
read this book. hope this helps.  spencer graves
Actually lme is better suited to nested designs than to crossed
designs and lme doesn't handle generalized linear mixed models.
GLMM from package lme4 does handle generalized linear mixed models but
does not handle crossed random effects easily.
I'm working on code that will handle nested, crossed and partially
crossed random effects but it will be some time before it appears in a
finished package


David Firth wrote:

Are there facilities in R or packages to estimate the parameters of
a (generalized) linear mixed model like this one: the design is
crossed, and response $y_{ij}$ relates to fixed and random effects
through a linear predictor

\[
  \eta_{ij} = \beta x_{ij} + U_i - U_j
\]
where $U_1, U_2, \ldots$ are iid $N(0, \tau^2)$.
Any suggestions would be welcomed.

David

Professor David Firth
Dept of Statistics
University of Warwick
Coventry CV4 7AL
United Kingdom
Voice: +44 (0)247 657 2581
Fax:   +44 (0)247 652 4532
Web:   http://www.warwick.ac.uk/go/dfirth
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--
Douglas Bates[EMAIL PROTECTED]
Statistics Department608/262-2598
University of Wisconsin - Madison
http://www.stat.wisc.edu/~bates/
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Re: [R] structured random effects

2004-02-06 Thread Douglas Bates 
Spencer Graves [EMAIL PROTECTED] writes:

   Have you considered lme?  For applications like this, I highly
 recommend Pinheiro and Bates (2000) Mixed-Effects Models in S and
 S-Plus (Springer).  I failed to produce anything with lme until I
 read this book. hope this helps.  spencer graves

Actually lme is better suited to nested designs than to crossed
designs and lme doesn't handle generalized linear mixed models.

GLMM from package lme4 does handle generalized linear mixed models but
does not handle crossed random effects easily.

I'm working on code that will handle nested, crossed and partially
crossed random effects but it will be some time before it appears in a
finished package

 
 
 David Firth wrote:
 
  Are there facilities in R or packages to estimate the parameters of
  a (generalized) linear mixed model like this one: the design is
  crossed, and response $y_{ij}$ relates to fixed and random effects
  through a linear predictor
 
  \[
\eta_{ij} = \beta x_{ij} + U_i - U_j
  \]
  where $U_1, U_2, \ldots$ are iid $N(0, \tau^2)$.
 
  Any suggestions would be welcomed.
 
  David
 
  Professor David Firth
  Dept of Statistics
  University of Warwick
  Coventry CV4 7AL
  United Kingdom
 
  Voice: +44 (0)247 657 2581
  Fax:   +44 (0)247 652 4532
  Web:   http://www.warwick.ac.uk/go/dfirth
 
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  https://www.stat.math.ethz.ch/mailman/listinfo/r-help
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  http://www.R-project.org/posting-guide.html
 
 
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-- 
Douglas Bates[EMAIL PROTECTED]
Statistics Department608/262-2598
University of Wisconsin - Madisonhttp://www.stat.wisc.edu/~bates/

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