[R-sig-ME] Questions on migrating from nlme to lme4
Douglas Bates
bates at stat.wisc.edu
Wed Jun 13 19:25:51 CEST 2007
Thanks for your messages, Robert. You mentioned posting to the
R-SIG-Mixed-Models list so I am taking the liberty of cc:ing the list
on this reply.
It is best to think of lme4 as a reimplementation of mixed models from
the ground up. I have been seeking a way of generalizing the theory
and computational methods for mixed models in a flexible, efficient
and extensible way. At the risk of being immodest I will say that I
think I have done so.
For linear mixed models the big difference between lme and lmer is
that lmer can handle models with crossed or partially crossed random
effects efficiently. In areas like the analysis of scores on
state-wide tests mandated by the No Child Left Behind act or in the
analysis of "breeding values" by animal scientists, large data sets
with a complex structure are the norm. The state-of-the-art methods
for sparse matrices that are used in lmer make fitting mixed models to
such data feasible.
In the process of examining linear mixed models in detail I eventually
arrived at a formulation of the linear mixed model that generalizes to
generalized linear mixed models and nonlinear mixed models in a way
that, to me at least, makes sense. The basic computational steps and
the representation of the models are sufficiently similar to share a
lot of the code.
Thus the lme4 package is not an upgrade of the nlme package. It
shares some characteristics with nlme but it is best to think of it as
a reimplementation, not an extension.
By the way, the theoretical development that I mentioned will be
described in the vignette "Theory and Computational Methods for Mixed
Models". The first few sections of that vignette are included in the
lme4_0.99875-1 package and there is more to come. Sorry for releasing
snapshots of a paper (or a book chapter, in this case). Because the
document changes between releases of the package there is not a stable
reference to cite. Once I have covered all the ground I want to cover
I will release a stable version as a technical report.
On 6/13/07, Robert Kushler <kushler at oakland.edu> wrote:
> Follow-up to previous message (included below):
> I discovered that "confint" can be used in place of "intervals", so item 2
> is taken care of.
There is a "confint" method for "lmList" objects (also discussed
below). I'm not sure I will produce one for "lmer" objects if the
degrees of freedom controversy is not resolved. Also I don't think
that symmetric intervals on variance components make sense and would
prefer not to produce them.
Generally I recommend using mcmcsamp to produce "confidence intervals"
on the variance components. Approximate (and symmetric) confidence
intervals on the fixed effects are reasonably accurate but such
intervals for the random effects can be poor approximations.
> I also gather that you allow (expect?) users to use the
> two libraries simultaneously. I assume you recommend loading nlme first,
> so the lmer version of common functions will be used.
No, I don't think it is a good idea to use nlme and lme4
simultaneously. At one point I had code in lme4 to give a warning if
you tried to load the lme4 package when the nlme package was already
loaded.
> The windows version of 0.99875-1 appeared today.
Good.
> I have a small bit of user feedback: lmList requires the "data" argument
> even when all of the needed variables exist in the global environment.
> I know it's good practice to construct a data frame (and advantageous to
> exploit the groupedData structure), but since lmer lets the user be lazy,
> why doesn't lmList do the same?
I haven't looked at that version of lmList for a long time. I suppose
that I am not generating a call to 'model.frame' and I should. I can
put that on the "To Do" list but it will be a long way down the list.
The answer to another of your questions about using groupedData
objects is that I do not encourage the use of groupedData objects with
lme4. They were an attempt to incorporate information on the grouping
structure with the data but they got too complicated. I'm in a "keep
it simple" mode in the development of the lme4 package. Also, the
groupedData objects use S3 classes and one of the purposes of the lme4
package is to explore the use of S4 classes and methods.
Another aspect of the groupedData objects is that the formulation,
like the formulation of lme itself, makes sense for nested groupings
but not for crossed or partially crossed groupings.
To some extent the purpose of defining the groupedData classes was to
make it easier to generate meaningful and informative graphics with
the trellis functions in S-PLUS. Deepayan Sarkar's development of
lattice package for R has incorporated so many new features that many
of the operations that required custom code in trellis are possible
transparently in lattice.
> Thanks again for whatever time you can spare, Rob Kushler
> Text of previous message:
> I am belatedly transitioning to lme4 and have some questions. Some of
> the following are things I used to show my students with the nlme library
> but have not yet figured out how to do with lme4. If some of this is on
> your to-do list, perhaps you could post a message to [R-sig-ME] about your
> plans. (I am eagerly awaiting the appearance of the 0.99875-1 zip file.)
Ahem, I believe you mean the nlme *package*. (Bear in mind that
Martin Maechler reads this list. :-)
> My general question is whether any of the following can be done in the current
> version of lme4 (and if so how), or if these features might be added ("soon",
> not so soon, or never). I know that I can show my students a "mixed" (sic)
> strategy using both the old and new libraries, but that's a bit awkward, and
> I'd like to convince them (and myself) that we can someday leave nlme behind
> with no (or few) regrets.
> If you have addressed these issues elsewhere, forgive me for not locating it.
> 1) The "correlation" and "weights" arguments of gls and lme were very useful.
> In particular, I'd like to fit the AR1 and "unstructured" models, and be
> able to "model heteroscedasticity".
The correlation and weights arguments haven't made it past the "keep
it simple" filter yet.
> 2) I understand your position on Wald inference for variance components, but
> the "intervals" function had other uses. In particular, I like to show
> the students "plot(intervals(lmListobject)) as a diagnostic tool. Do you
> no longer recommend this?
I do think that a plot of the confidence intervals from an lmList
object is a good idea. (For one thing, those confidence intervals are
well-defined.) I have code that looks like
###################################################
### chunk number 3: Sl1
###################################################
print(plot(confint(lmList(Reaction ~ Days | Subject, sleepstudy),
pooled = TRUE), order = 1))
and it appears to work in current versions of lme4.
> 3) Do you plan to give the inference tools in "languageR" an official blessing
> by incorporating them into lme4?
I certainly think they are a good idea and I worked with Harald Baayen
a bit on the development of them last January when I visited with him.
However, I still want to think through the inference issues before
incorporating my final word about inference in the lme4 package. One
of the annoying aspects others find in working with me is that I am
very slow on design and theoretical development. I like to think that
the good side of this is that I am thorough.
The lme4 package and the theoretical work behind it have had a long
gestation period - one that probably will extend for a long time to
come. However, I like the results and I am comfortable with the idea
that I have carefully considered many, many aspects of the model and
computational methods before incorporating them.
> 4) Your R-news article in May 2005 shows "anova" producing F's and p-values,
> but that no longer seems to be the case.
Indeed. I realize that calculation of p-values is immensely important
to some people but doing it properly for unbalanced designs is
extremely complicated and, in the areas that I am most concerned with
(large data sets with complex structure), it's a non-issue. When you
have data on a half-million students in 2500 schools whatever
calculation you use for the degrees of freedom will produce a number
so large that the exact number is irrelevant.
If someone wants to work out how to calculate an approximate
denomination degrees of freedom, and they can get the "degrees of
freedom police" (John Maindonald and Peter Dalgaard) to approve the
result, I'll put the degrees of freedom back in. Until then, you just
have to do what several people in the psycholinguistics area have
done, which is to uncomment the lines for the denominator degrees of
freedom upper bound in the code for the anova method.
> In this connection, do you have
> plans to implement the "potentially useful approximate D.F." mentioned
> in mcmcsamp.R?
I'm not sure what code you are referring to there. Is it in the
languageR package?
I imagine the approximation in question is from the trace of the "hat"
matrix. If you look at the Theory vignette you will see I view the
basic calculation in mixed models as the solution of a penalized least
squares problem. For an unpenalized least squares problem the
residual degrees of freedom is n minus the trace of the hat matrix.
Frequently in penalized least squares problems the same approximation
is used.
That quantity also appears in the definition of the Deviance
Information Criterion (DIC) by Spiegelhalter et al. for this model.
However, it does not reproduce the results from balanced experiments
with small data sets and I am beginning to have some doubts about
whether this calculation is the best way to go.
> 5) Do you no longer recommend the use of groupedData objects, or do you plan
> to add them to lme4?
See above.
> I'm sure I'll think of additional questions, but that's (more than) enough
> for now.
> Thanks and regards, Rob Kushler (Oakland University)
Thanks for your questions. I hope my answers were helpful.
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