[R-sig-ME] [R] lmer syntax, matrix of (grouped) covariates?
maechler at stat.math.ethz.ch
Tue Aug 19 08:59:58 CEST 2008
>>>>> "AM" == Aaron Mackey <ajmackey at gmail.com>
>>>>> on Mon, 18 Aug 2008 15:41:20 -0400 writes:
AM> Dear Dr. Bates, Thanks for the info about the SIG on
AM> mixed models.
AM> A followup question: if I did not want a generalized
AM> variance-covariance matrix, and instead wanted to assume
AM> independence between grouped covariates, then instead of
AM> (3n-n^2)/2 variance-covariance parameters per grouping
AM> (of size n), I'd have only n variance parameters, yes?
AM> Would that be specified (x1|fac) + (x2|fac) instead of
AM> (x1 + x2 | fac)?
AM> And either way I formulate it, my syntax question
AM> remains: is there a way to write the formula that is
AM> generic for a given covariate matrix of unspecified
AM> dimension? Said another way, how would you write a lmer
AM> "wrapper" function that was given a matrix of
AM> covariates? How would you "unpack" the columns of that
AM> matrix to generate the formula required for lmer()?
AM> Would you build up a formula string programmatically,
AM> and then "eval" it?
yes / "kind of" :
I would build the formula programmatically
but then use as.formula(.) or possibly in some cases parse().
Package 'sfsmisc' has a utility wrapFormula() that I had need
for at some time:
myF <- wrapFormula(Fertility ~ . , data = swiss)
## Fertility ~ s(Agriculture) + s(Examination) + s(Education) +
## s(Catholic) + s(Infant.Mortality)
and you can look at the short definition of wrapFormula
to see how I use gsub(.) on character strings to construct the
Martin Maechler, ETH Zurich
AM> Thanks again,
AM> On Mon, Aug 18, 2008 at 3:21 PM, Douglas Bates
AM> <bates at stat.wisc.edu> wrote:
>> On Mon, Aug 18, 2008 at 11:20 AM, Aaron Mackey
>> <ajmackey at gmail.com> wrote: > I have a fairly large
>> >> length(Y) >  3051 >> dim(covariates) >  3051 211
>> > All of these 211 covariates need to be nested
>> hierarchically within a > grouping "class", of which
>> there are 8. I have an accessory vector, " > cov2class"
>> that specifies the mapping between covariates and the 8
>> > Now, I understand I can break all this information up
>> into individual > vectors (cov1, cov2, ..., cov211,
>> class1, class2, ..., class8), and do > something like
>> > model <- lmer(Y ~ 1 + cov1 + cov2 + ... + cov211 + >
>> (cov1 + cov2 + ... | class1) + > (...) + > (... + cov210
>> + cov211 | class8)
>> > But I'd like keep things syntactically simpler, and use
>> the covariates > and cov2class > variables directly. I
>> haven't been able to find the right syntactic sugar > to
>> get this done.
>> Before considering the syntax you should consider the
>> complexity of the model. Even with 3000+ observations
>> estimating fixed effects for over 200 covariates is a
>> risky business. To follow that up by incorporating
>> dozens of covariates in a vector-valued random effects
>> term with a general variance covariance matrix is not
>> likely to be productive. When estimating variances and
>> covariances of random effects the complexity of the
>> estimation problem increases according to the square of
>> the dimension of each random-effects vector. For a term
>> of the form (1|fac) there is one variance-covariance
>> parameter to estimate. For a term of the form (1+x|fac)
>> there are 3 such parameters to estimate, (1+x1+x2|fac)
>> requires 6, etc. Assuming that your 211 covariates are
>> more-or-less equally distributed among the 8 classes you
>> would have about 27 per class which means that you have
>> 378 variance-covariance parameters to estimate for each
>> of your 8 classes. Even with 3000+ observations it would
>> be optimistic to expect to estimate that many
>> variance-covariance parameters.
>> You may want to move your question to the
>> R-SIG-Mixed-Models mailing list and follow up on the
>> model specification there.
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