Re: [R] lsmeans

From: John Fox <jfox_at_mcmaster.ca>
Date: Sun, 08 Jun 2008 15:29:12 -0400

Dear Frank,

This point is also correct, but whether this is a serious issue depends upon the structure of the model. With a linear predictor and, in a generalized linear model, expressing fitted values on the scale of the linear predictor, differences are preserved regardless of the "typical" values chosen. Often, interest is primarily in the "shape" of an interaction.

Regards,
 John



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

> -----Original Message-----
> From: Frank E Harrell Jr [mailto:f.harrell_at_vanderbilt.edu]
> Sent: June-08-08 3:13 PM
> To: Douglas Bates
> Cc: John Fox; Dieter Menne; r-help_at_stat.math.ethz.ch
> Subject: Re: [R] lsmeans
>
> Douglas Bates wrote:
> > 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.
>
> Well put Doug. I would add another condition, which I don't know how to
> state precisely. The settings for the other terms, which are usually
> marginal medians, modes, or means, must make sense when considered
> jointly. Frequently when all adjustment covariates are set to overall
> marginal means the resulting "subject" is very atypical.
>
> To me much of the problem is solved one one develops a liking for
> predicted values and differences in them.

>
> Frank
>
> >
> > 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.
> >>
> >
> > ______________________________________________
> > 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.
> >
>
>
> --
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics Vanderbilt University



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