Dear Peter, Doug, and Felipe, My effects package (on CRAN, also see the article at http://www.jstatsoft.org/counter.php?id=75&url=v08/i15/effect-displays-revis ed.pdf) will compute and graph adjusted effects of various kinds for linear and generalized linear models -- generalizing so-called "least-squares means" (or "population marginal means" or "adjusted means").
A couple of comments: By default, the all.effects() function in the effects package computes effects for high-order terms in the model, absorbing terms marginal to them. You can ask the effect() function to compute an effect for a term that's marginal to a higher-order term, and it will do so with a warning, but this is rarely sensible. Peter's mention of floating variances (or quasi-variances) in this context is interesting, but what would most like to see, I think, are the quasi-variances for the adjusted effects, that is for terms merged with their lower-order relatives. These, for example, are unaffected by contrast coding. How to define reasonable quasi-variances in this context has been puzzling me for a while. Regards, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Peter Dalgaard > Sent: Friday, September 23, 2005 10:23 AM > To: Douglas Bates > Cc: Felipe; [email protected] > Subject: Re: [R] Are least-squares means useful or appropriate? > > Douglas Bates <[EMAIL PROTECTED]> writes: > > > On 9/20/05, Felipe <[EMAIL PROTECTED]> wrote: > > > -----BEGIN PGP SIGNED MESSAGE----- > > > Hash: SHA1 > > > > > > Hi. > > > My question was just theoric. I was wondering if someone who were > > > using SAS and R could give me their opinion on the topic. I was > > > trying to use least-squares means for comparison in R, but then I > > > found some indications against them, and I wanted to know if they > > > had good basis (as I told earlier, they were not much detailed). > > > Greetings. > > > > > > Felipe > > > > As Deepayan said in his reply, the concept of least squares > means is > > associated with SAS and is not generally part of the theory > of linear > > models in statistics. My vague understanding of these (I > too am not a > > SAS user) is that they are an attempt to estimate the > "mean" response > > for a particular level of a factor in a model in which that > factor has > > a non-ignorable interaction with another factor. There is > no clearly > > acceptable definition of such a thing. > > (PD goes and fetches the SAS manual....) > > Well, yes. it'll do that too, although only if you ask for > the lsmeans of A when an interaction like A*B is present in > the model. This is related to the tests of main effects when > an interaction is present using type III sums of squares, > which has been beaten to death repeatedly on the list. In > both cases, there seems to be an implicit assumption that > categorical variables by nature comes from an underlying > fully balanced design. > > If the interaction is absent from the model, the lsmeans are > somewhat more sensible in that they at least reproduce the > parameter estimates as contrasts between different groups. > All continuous variables in the design will be set to their > mean, but values for categorical design variables are > weighted inversely as the number of groups. So if you're > doing an lsmeans of lung function by smoking adjusted for age > and sex you get estimates for the mean of a population of > which everyone has the same age and half are male and half > are female. This makes some sense, but if you do it for sex > adjusting for smoking and age, you are not only forcing the > sexes to smoke equally much, but actually adjusting to > smoking rates of 50%, which could be quite far from reality. > > The whole operation really seems to revolve around 2 things: > > (1) pairwise comparisons between factor levels. This can alternatively > be done fairly easily using parameter estimates for the relevant > variable and associated covariances. You don't really need all the > mumbo-jumbo of adjusting to particular values of other variables. > > (2) plotting effects of a factor with error bars as if they were > simple group means. This has some merit since the standard > parametrizations are misleading at times (e.g. if you choose the > group with the least data as the reference level, std. err. for > the other groups will seem high). However, it seems to me that > concepts like floating variances (see float() in the Epi package) > are more to the point. > > > R is an interactive language where it is a simple matter to fit a > > series of models and base your analysis on a model that is > > appropriate. An approach of "give me the answer to any possible > > question about this model, whether or not it make sense" is > > unnecessary. > > > > In many ways statistical theory and practice has not caught up with > > statistical computing. There are concepts that are > regarded as part > > of established statistical theory when they are, in fact, > > approximations or compromises motivated by the fact that you can't > > compute the answer you want - except now you can compute > it. However, > > that won't stop people who were trained in the old system from > > assuming that things *must* be done in that way. > > > > In short, I agree with Deepayan - the best thing to do is to ask > > someone who uses SAS and least squares means to explain to you what > > they are. > > > > ______________________________________________ > > [email protected] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > > > -- > O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: > (+45) 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: > (+45) 35327907 > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
