From: John Fox <jfox_at_mcmaster.ca>

Date: Sun 27 Aug 2006 - 23:51:57 EST

John Fox

Department of Sociology

McMaster University

Hamilton, Ontario

Canada L8S 4M4

905-525-9140x23604

http://socserv.mcmaster.ca/jfox

R-help@stat.math.ethz.ch 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. Received on Sun Aug 27 23:58:28 2006

Date: Sun 27 Aug 2006 - 23:51:57 EST

A complete explanation of the issues that you raise is awkward in an email, so I'll address your questions briefly. Section 8.2 of my text, Applied Regression Analysis, Linear Models, and Related Methods (Sage, 1997) has a detailed discussion.

(1) In balanced designs, so-called "Type I," "II," and "III" sums of squares are identical. If the STATA manual says that Type II tests are only appropriate in balanced designs, then that doesn't make a whole lot of sense (unless one believes that Type-II tests are nonsense, which is not the case).

(2) One should concentrate not directly on different "types" of sums of squares, but on the hypotheses to be tested. Sums of squares and F-tests should follow from the hypotheses. Type-II and Type-III tests (if the latter are properly formulated) test hypotheses that are reasonably construed as tests of main effects and interactions in unbalanced designs. In unbalanced designs, Type-I sums of squares usually test hypotheses of interest only by accident.

(3) Type-II sums of squares are constructed obeying the principle of marginality, so the kinds of contrasts employed to represent factors are irrelevant to the sums of squares produced. You get the same answer for any full set of contrasts for each factor. In general, the hypotheses tested assume that terms to which a particular term is marginal are zero. So, for example, in a three-way ANOVA with factors A, B, and C, the Type-II test for the AB interaction assumes that the ABC interaction is absent, and the test for the A main effect assumes that the ABC, AB, and AC interaction are absent (but not necessarily the BC interaction, since the A main effect is not marginal to this term). A general justification is that we're usually not interested, e.g., in a main effect that's marginal to a nonzero interaction.

(4) Type-III tests do not assume that terms higher-order to the term in question are zero. For example, in a two-way design with factors A and B, the type-III test for the A main effect tests whether the population marginal means at the levels of A (i.e., averaged across the levels of B) are the same. One can test this hypothesis whether or not A and B interact, since the marginal means can be formed whether or not the profiles of means for A within levels of B are parallel. Whether the hypothesis is of interest in the presence of interaction is another matter, however. To compute Type-III tests using incremental F-tests, one needs contrasts that are orthogonal in the row-basis of the model matrix. In R, this means, e.g., using contr.sum, contr.helmert, or contr.poly (all of which will give you the same SS), but not contr.treatment. Failing to be careful here will result in testing hypotheses that are not reasonably construed, e.g., as hypotheses concerning main effects.

(5) The same considerations apply to linear models that include quantitative predictors -- e.g., ANCOVA. Most software will not automatically produce sensible Type-III tests, however.

I hope this helps,

John

John Fox

Department of Sociology

McMaster University

Hamilton, Ontario

Canada L8S 4M4

905-525-9140x23604

http://socserv.mcmaster.ca/jfox

> -----Original Message-----

*> From: r-help-bounces@stat.math.ethz.ch
**> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Amasco
**> Miralisus
**> Sent: Saturday, August 26, 2006 5:07 PM
**> To: r-help@stat.math.ethz.ch
**> Subject: [R] Type II and III sum of square in Anova (R, car package)
**>
**> Hello everybody,
**>
**> I have some questions on ANOVA in general and on ANOVA in R
**> particularly.
**> I am not Statistician, therefore I would be very appreciated
**> if you answer it in a simple way.
**>
**> 1. First of all, more general question. Standard anova()
**> function for lm() or aov() models in R implements Type I sum
**> of squares (sequential), which is not well suited for
**> unbalanced ANOVA. Therefore it is better to use
**> Anova() function from car package, which was programmed by
**> John Fox to use Type II and Type III sum of squares. Did I
**> get the point?
**>
**> 2. Now more specific question. Type II sum of squares is not
**> well suited for unbalanced ANOVA designs too (as stated in
**> STATISTICA help), therefore the general rule of thumb is to
**> use Anova() function using Type II SS only for balanced ANOVA
**> and Anova() function using Type III SS for unbalanced ANOVA?
**> Is this correct interpretation?
**>
**> 3. I have found a post from John Fox in which he wrote that
**> Type III SS could be misleading in case someone use some
**> contrasts. What is this about?
**> Could you please advice, when it is appropriate to use Type
**> II and when Type III SS? I do not use contrasts for
**> comparisons, just general ANOVA with subsequent Tukey
**> post-hoc comparisons.
**>
**> Thank you in advance,
**> Amasco
**>
**> [[alternative HTML version deleted]]
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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. Received on Sun Aug 27 23:58:28 2006

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