[R] Unbalanced Anova: What is the best approach?
John Fox
jfox at mcmaster.ca
Sun Apr 3 17:35:01 CEST 2011
Dear Krishna,
> -----Original Message-----
> From: Krishna Kirti Das [mailto:krishnakirti at gmail.com]
> Sent: April-03-11 10:36 AM
> To: John Fox
> Cc: r-help at 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 <jfox at 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-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org]
> > On Behalf Of Krishna Kirti Das
> > Sent: April-03-11 3:25 AM
> > To: r-help at 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]]
> >
> > ______________________________________________
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>
>
>
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