[R-sig-ME] Modeling correlation structure in mixed models

Gabor Grothendieck ggrothendieck at gmail.com
Sat Jun 27 11:35:53 CEST 2009

In addition to the sources already mentioned there are chapters on
mixed models in the books associated with the mgcv and DAAG
R packages.

On Fri, Jun 26, 2009 at 10:48 PM, Kingsford
Jones<kingsfordjones at gmail.com> wrote:
> Hi Phillip,
> Welcome.  Although I'm a fan of PROC MIXED, I think you'll find doing
> your mixed modeling in R a relative joy.  Unfortunately, to experience
> the joy one must learn to navigate the byzantine labyrinth of
> documentation that has grown from this community effort.  A few leads
> are offered below...
> On Fri, Jun 26, 2009 at 4:42 PM, Phillip
> Chapman<pchapman at stat.colostate.edu> wrote:
>> Hi All,
>> I have been trying to learn mixed models in R by reading the books by
>> Pinheiro and Bates; Faraway (both linear models books); and Crawley (R
>> Book), but I would appreciate some guidance from the more experience R
>> users. (I have a fair amount of experience with mixed models in SAS.)
>> 1. Is there another (other than the above) suggested reference for
>> understanding the workings of the nlme and lme4 libraries?
> P&B is of course the authoritative reference for the nlme package, and
> Doug has mentioned on this list that in his (limited) spare time he is
> working on a book to accompany lme4.  The lme4 package does come with
> several vignettes that can be accessed from R by a call to the
> vignette function or by simply opening the pdfs in
> yourRlibrary/lme4/doc/.  There is also a vignette in the SASmixed
> package called 'lmer for SAS PROC MIXED Users'.  Other helpful
> references can be found on the CRAN contributed documentation section,
> such as the Mixed Models Web Appendix to John Fox's book.  I haven't
> read Gelman and Hill's Data Analysis and Regression using
> Multilevel/Hierarchical models, but as I understand it they user lmer
> extensively, with wrappers for Bayesian inferences.  Also, Harald
> Baayen has a freely available draft of a book on analyzing linguistic
> data that includes many lmer examples:
> http://www.ualberta.ca/~baayen/publications.html
> Googling the following may also be useful:
> lmer filetype:pdf
> Here are some of Doug's documents that show up:
> www.stat.wisc.edu/~bates/reports/MixedComp.pdf
> user2007.org/program/presentations/bates.pdf
> http://www.jstatsoft.org/v20/i02
> www.stat.wisc.edu/~bates/IMPS2008/lme4D.pdf
>> 2. Is it the case that lme accepts correlated structures ONLY in the error
>> term? I have problems in which I would like model random effects (such as
>> year) using a random term with an autocorrelated structure. In SAS I use
>> options to the “repeated” statement to add correlation structure to the
>> error term, and I use options to the “random” statement to give correlation
>> structure to the other random effects. I haven’t found anything in lme or
>> lmer that allows me to specify correlated random effects. gee only allows
>> correlation structure in the error term and does not allow random effects.
> This is something that I have wondered about as well -- as far as I
> know one can only specify a correlation structure for the error
> covariance matrix, and only using the nlme package (not lme4).
> However, given that there are thousands of R packages available I
> would not be surprised if someone's already coded up a way to do this
> (perhaps in one of the spatial packages using a Bayesian approach,
> such as spBayes or geoRglm?)
>> 3. All of the examples of random effects in lme seem to have nested error
>> structures. Is it the case that lme does not allow crossed random effects?
>> lmer allows much more flexible specification of random effects, but I don’t
>> see anything that allows correlated error structures.
> Although nlme is designed for nested data, crossed random effects can
> be specified using a combination of pdBlocked and pdIdent objects (see
> page 163 of P&B).  However it's an awkward specification and the
> fitting can be slow (IIRC). On the other hand lmer offers elegant
> methods of specifying crossed models and speedy methods for fitting
> them.
>> Thanks in advance,
> You're welcome -- hope it helped,
> Kingsford Jones
>> Phil Chapman
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