Dear Krishna, > -----Original Message----- > From: Krishna Kirti Das [mailto:krishnaki...@gmail.com] > Sent: April-03-11 10:36 AM > To: John Fox > Cc: r-help@r-project.org > Subject: Re: [R] Unbalanced Anova: What is the best approach? > > Thank you, John. > > Yes, your answers do help. For me it's mainly about getting familiar with > the "R" way of doing things. > > Thus your response also confirms what I suspected, that there is no > explicit user-interface (at least one that is widely used) in terms of > functions/packages that represents an unbalanced design in the same way > that aov would represent a balanced one. Analyzing balanced and unbalanced > data are obviously possible, but with balanced designs via aov what has to > be done is intuitive within the language but unintuitive for unbalanced > designs.
I don't agree with your characterization. For example, the representation of a two-way crossed ANOVA model as an R model formula is precisely the same for balanced and unbalanced data: for response Y and factors A and B, Y ~ A*B. Moreover, the issue of how to formulate tests is independent of the software you use. > > I did notice that this question gets asked several times and in slightly > different ways, and I think the lack of an interface that represents an > unbalanced design in the same way aov represents balanced designs is why > the question will probably keep getting asked again. I suspect that the issue gets asked repeatedly for two reasons: (1) More fundamentally, I believe that the general level of understanding of hypothesis tests in unbalanced data is low; (2) people don't necessarily read previous posts to r-help. > > I had mentioned nlme and lme4 because I saw in some of the discussions > that using those were recommended for working with unbalanced designs. And > specifying random effects with zero variance, for example, would probably > serve my purposes. I don't think that either lme() or lmer() will allow you to fit a model without random effects, but even if they did there wouldn't be much sense in doing so. You can compute a mean with lm() or glm(), but would you? Best, John > > Thank you for your help. > > Sincerely, > > Krishna > > On Sun, Apr 3, 2011 at 7:28 AM, John Fox <j...@mcmaster.ca> wrote: > > > Dear Krishna, > > Although it's difficult to explain briefly, I'd argue that balanced > and > unbalanced ANOVA are not fundamentally different, in that the focus > should > be on the hypotheses that are tested, and these are naturally > expressed as > functions of cell means and marginal means. For example, in a two-way > ANOVA, > the null hypotheses of no interaction is equivalent to parallel > profiles of > cell means for one factor across levels of the other. What is > different, > though, is that in a balanced ANOVA all common approaches to > constructing an > ANOVA table coincide. > > Without getting into the explanation in detail (which you can find in > a text > like my Applied Regression Analysis and Generalized Linear Models), > so-called type-I (or sequential) tests, such as those performed by > the > standard anova() function in R, test hypotheses that are rarely of > substantive interest, and, even when they are, are of interest only > by > accident. So-called type-II tests, such as those performed by default > by the > Anova() function in the car package, test hypotheses that are almost > always > of interest. Type-III tests, which the Anova() function in car can > perform > optionally, require careful formulation of the model for the > hypotheses > tested to be sensible, and even then have less power than > corresponding > type-II tests in the circumstances in which a test would be of > interest. > > Since you're addressing fixed-effects models, I'm not sure why you > introduced nlme and lme4 into the discussion, but I note that Anova() > in the > car package has methods that can produce type-II and -III Wald tests > for the > fixed effects in mixed models fit by lme() and lmer(). > > Your question has been asked several times before on the r-help list. > For > example, if you enter terms like "type-II" or "unbalanced ANOVA" in > the > RSeek search engine and look under the "Support Lists" tab, you'll > see many > hits -- e.g., > <Mhttps://stat.ethz.ch/pipermail/r-help/2006-August/111927.html>. > > I hope this helps, > John > > -------------------------------- > John Fox > Senator William McMaster > Professor of Social Statistics > Department of Sociology > McMaster University > Hamilton, Ontario, Canada > http://socserv.mcmaster.ca/jfox > > > > > > -----Original Message----- > > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] > > On Behalf Of Krishna Kirti Das > > Sent: April-03-11 3:25 AM > > To: r-help@r-project.org > > Subject: [R] Unbalanced Anova: What is the best approach? > > > > I have a three-way unbalanced ANOVA that I need to calculate (fixed > > effects plus interactions, no random effects). But word has it that > aov() > > is good only for balanced designs. I have seen a number of > different > > recommendations for working with unbalanced designs, but they seem > to > > differ widely (car, nlme, lme4, etc.). So I would like to know what > is the > > best or most usual way to go about working with unbalanced designs > and > > extracting a reliable ANOVA table from them in R? > > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@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@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.
