[R] lsmeans

[John Fox]

Dear Doug,

Your point is correct, of course, but if people are interested in computing
marginal means (or marginal cell means), then they can do so simply and
don't need a statistical model. I think that when such a model is fit,
interest is typically in conditioning on the other explanatory variables.

(Also see my responses to Hadley and Frank's points.)

Regards,
 John

------------------------------
John Fox, Professor
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
web: socserv.mcmaster.ca/jfox

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On
> Behalf Of Douglas Bates
> Sent: June-08-08 1:58 PM
> To: John Fox
> Cc: Dieter Menne; r-help at stat.math.ethz.ch
> Subject: Re: [R] lsmeans
> 
> On 6/7/08, John Fox <jfox at mcmaster.ca> wrote:
> > Dear Dieter,
> >
> >  I don't know whether I qualify as a "master," but here's my brief take
on
> >  the subject: First, I dislike the term "least-squares means," which
seems
> to
> >  me like nonsense. Second, what I prefer to call "effect displays" are
just
> >  judiciously chosen regions of the response surface of a model, meant to
> >  clarify effects in complex models. For example, a two-way interaction
is
> >  displayed by absorbing the constant and main-effect terms in the
> interaction
> >  (more generally, absorbing terms marginal to a particular term) and
> setting
> >  other terms to typical values. A table or graph of the resulting fitted
> >  values is, I would argue, easier to grasp than the coefficients, the
> >  interpretation of which can entail complicated mental arithmetic.
> 
> I like that explanation, John.
> 
> As I'm sure you are aware, the key phrase in what you wrote is
> "setting other terms to typical values".  That is, these are
> conditional cell means, yet they are almost universally misunderstood
> - even by statisticians who should know better - to be marginal cell
> means.  A more subtle aspect of that phrase is the interpretation of
> "typical".  The user is not required to specify these typical values -
> they are calculated from the observed data.
> 
> If there are no interactions with the "other terms" and if the values
> chosen for those other terms based on the observed data are indeed
> typical of the values for which we wish to make inferences with the
> model then these conditional cell means may tell us something about
> the marginal cell means.  But if either of those conditions fails then
> these conditional means can be very different from the marginal means.
> 
> I wouldn't have any problem at all with providing conditional cell
> means, especially if the user were required to specify the values at
> which to fix the other terms in the model, but that is not what people
> think they are getting.  I don't want to encourage them in their
> delusions by letting them think i can evaluate marginal cell means as
> a single, conditional evaluation.
> 
> >  > -----Original Message-----
> >  > From: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org]
> >  On
> >  > Behalf Of Dieter Menne
> >  > Sent: June-07-08 4:36 AM
> >  > To: r-help at stat.math.ethz.ch
> >  > Subject: Re: [R] lsmeans
> >  >
> >  > John Fox <jfox <at> mcmaster.ca> writes:
> >  >
> >  > > I intend at some point to extend the effects package to linear and
> >  > > generalized linear mixed-effects models, probably using lmer()
rather
> >  > > than lme(), but as you discovered, it doesn't handle these models
now.
> >  > >
> >  > > It wouldn't be hard, however, to do the computations yourself,
using
> >  > > the coefficient vector for the fixed effects and a suitably
> constructed
> >  > > model-matrix to compute the effects; you could also get standard
> errors
> >  > > by using the covariance matrix for the fixed effects.
> >  > >
> >  >
> >  > >> Douglas Bates:
> >  > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2007q2/000222.html
> >  > >>
> >  > My big problem with lsmeans is
> >  > that I have never been able to understand how they should be
> >  > calculated and, more importantly, why one should want to calculate
> >  > them.  In other words, what do lsmeans represent and why should I be
> >  > interested in these particular values?
> >  > >>
> >  >
> >  > Truly Confused, torn apart by the Masters
> >  >
> >  > Dieter
> >  >
> >  > ______________________________________________
> >  > R-help at r-project.org 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.
> >
> >  ______________________________________________
> >  R-help at r-project.org 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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