Dear John - thank you for your detailed answer and help.
Your answer encourages me to ask further: by choosing different contrasts,
what are the different hypothesis which are being tested? (or put
differently - should I prefer contr.sum over contr.poly or contr.helmert,
or does this makes no difference ?)
How should this question be approached/answered ?
I see in the ?contrasts in R that the referenced reading is:
"Chambers, J. M. and Hastie, T. J. (1992) *Statistical models.* Chapter 2
of *Statistical Models in S* eds J. M. Chambers and T. J. Hastie, Wadsworth
& Brooks/Cole."
Yet I must admit I don't have this book readily available (not on the web,
nor in my local library), so other recommended sources would be of great
help.
For future reference I add here a some tinkering of the code to show how
implementing different contrasts will resort in different SS type III
analysis results:
phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
levels=c("pretest", "posttest", "followup"))
hour <- ordered(rep(1:5, 3))
idata <- data.frame(phase, hour)
contrasted.treatment <- C(OBrienKaiser$treatment, "contr.treatment")
mod.ok.contr.treatment <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~
contrasted.treatment*gender, data=OBrienKaiser)
contrasted.treatment <- C(OBrienKaiser$treatment, "contr.helmert")
mod.ok.contr.helmert <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~
contrasted.treatment*gender, data=OBrienKaiser)
contrasted.treatment <- C(OBrienKaiser$treatment, "contr.poly")
mod.ok.contr.poly <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~
contrasted.treatment*gender, data=OBrienKaiser)
contrasted.treatment <- C(OBrienKaiser$treatment, "contr.sum")
mod.ok.contr.sum <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~
contrasted.treatment*gender, data=OBrienKaiser)
# this is one result:
(Anova(mod.ok.contr.treatment, idata=idata, idesign=~phase*hour, type =
"III"))
# all of the other contrasts will now give the same outcome: (does that
mean there shouldn't be a preference of using one over the other ?)
(Anova(mod.ok.contr.helmert, idata=idata, idesign=~phase*hour, type =
"III"))
(Anova(mod.ok.contr.poly, idata=idata, idesign=~phase*hour, type = "III"))
(Anova(mod.ok.contr.sum, idata=idata, idesign=~phase*hour, type = "III"))
With regards,
Tal
On Sat, Feb 14, 2009 at 7:09 PM, John Fox <j...@mcmaster.ca> wrote:
> Dear Tal,
>
> > -----Original Message-----
> > From: Tal Galili [mailto:tal.gal...@gmail.com]
> > Sent: February-14-09 10:23 AM
> > To: John Fox
> > Cc: Peter Dalgaard; Nils Skotara; r-help@r-project.org; Michael Friendly
> > Subject: Re: [R] Anova and unbalanced designs
> >
> > Hello John and other R mailing list members.
> >
> > I've been following your discussions regarding the Anova command for the
> SS
> > type 2/3 repeated measures Anova, and I have a question:
> >
> > I found that when I go from using type II to using type III, the summary
> > model is suddenly added with an "intercept" term (example in the end of
> the
> > e-mail). So my question is
> > 1) why is this "intercept" term added (in SS type "III" vs the type
> "II")?
>
> The computational approach taken in Anova() makes it simpler to include the
> intercept in the "type-III" tests and not to include it in the "type-II"
> tests.
>
> > 2) Can/should this "intercept" term be removed ? (or how should it be
> > interpreted ?)
>
> The test for the intercept is rarely of interest. A "type-II" test for the
> intercept would test that the unconditional mean of the response is 0; a
> "type-III" test for the intercept would test that the constant term in the
> full model fit to the data is 0. The latter depends upon the
> parametrization
> of the model (in the case of an ANOVA model, what kind of "contrasts" are
> used). You state that the example that you give is taken from ?Anova but
> there's a crucial detail that's omitted: The help file only gives the
> "type-II" tests; the "type-III" tests are also reasonable here, but they
> depend upon having used "contr.sum" (or another set of contrasts that's
> orthogonal in the row basis of the model matrix) for the between-subject
> factors, treatment and gender. This detail is in the data set:
>
> > OBrienKaiser$gender
> [1] M M M F F M M F F M M M F F F F
> attr(,"contrasts")
> [1] contr.sum
> Levels: F M
>
> > OBrienKaiser$treatment
> [1] control control control control control A A A A
> B B
> [12] B B B B B
> attr(,"contrasts")
> [,1] [,2]
> control -2 0
> A 1 -1
> B 1 1
> Levels: control A B
>
> With proper contrast coding, the "type-III" test for the intercept tests
> that the mean of the cell means (the "grand mean") is 0.
>
> Had the default dummy-coded contrasts (from contr.treatment) been used, the
> tests would not have tested reasonable hypotheses. My advice, from the help
> file: "Be very careful in formulating the model for type-III tests, or the
> hypotheses tested will not make sense."
>
> I hope this helps,
> John
>
> >
> > My purpose is to be able to use the Anova for analyzing an experiment
> with
> a
> > 2 between and 3 within factors, where the between factors are not
> balanced,
> > and the within factors are (that is why I can't use the aov command).
> >
> >
> > #---code start
> >
> > #---code start
> >
> > #---code start
> >
> > # (taken from the ?Anova help file)
> >
> > phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
> > levels=c("pretest", "posttest", "followup"))
> > hour <- ordered(rep(1:5, 3))
> > idata <- data.frame(phase, hour)
> > idata
> > mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
> > post.1, post.2, post.3, post.4, post.5,
> > fup.1, fup.2, fup.3, fup.4, fup.5) ~
> treatment*gender,
> > data=OBrienKaiser)
> >
> > # now we have two options
> > # option one is to use type II:
> >
> > (av.ok <- Anova(mod.ok, idata=idata, idesign=~phase*hour, type = "II"))
> >
> >
> > #output:
> > Type II Repeated Measures MANOVA Tests: Pillai test statistic
> > Df test stat approx F num Df den Df Pr(>F)
> > treatment 2 0.4809 4.6323 2 10 0.0376868
> *
> > gender 1 0.2036 2.5558 1 10 0.1409735
> > treatment:gender 2 0.3635 2.8555 2 10 0.1044692
> > phase 1 0.8505 25.6053 2 9 0.0001930
> ***
> > treatment:phase 2 0.6852 2.6056 4 20 0.0667354
> .
> > gender:phase 1 0.0431 0.2029 2 9 0.8199968
> > treatment:gender:phase 2 0.3106 0.9193 4 20 0.4721498
> > hour 1 0.9347 25.0401 4 7 0.0003043
> ***
> > treatment:hour 2 0.3014 0.3549 8 16 0.9295212
> > gender:hour 1 0.2927 0.7243 4 7 0.6023742
> > treatment:gender:hour 2 0.5702 0.7976 8 16 0.6131884
> > phase:hour 1 0.5496 0.4576 8 3 0.8324517
> > treatment:phase:hour 2 0.6637 0.2483 16 8 0.9914415
> > gender:phase:hour 1 0.6950 0.8547 8 3 0.6202076
> > treatment:gender:phase:hour 2 0.7928 0.3283 16 8 0.9723693
> > ---
> > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> >
> >
> > # option two is to use type III, and then get an added intercept term:
> > (av.ok <- Anova(mod.ok, idata=idata, idesign=~phase*hour, type = "III"))
> >
> >
> > # here is the output:
> > Type III Repeated Measures MANOVA Tests: Pillai test statistic
> > Df test stat approx F num Df den Df Pr(>F)
> > (Intercept) 1 0.967 296.389 1 10 9.241e-09
> ***
> > treatment 2 0.441 3.940 2 10 0.0547069
> .
> > gender 1 0.268 3.659 1 10 0.0848003
> .
> > treatment:gender 2 0.364 2.855 2 10 0.1044692
> > phase 1 0.814 19.645 2 9 0.0005208
> ***
> > treatment:phase 2 0.696 2.670 4 20 0.0621085
> .
> > gender:phase 1 0.066 0.319 2 9 0.7349696
> > treatment:gender:phase 2 0.311 0.919 4 20 0.4721498
> > hour 1 0.933 24.315 4 7 0.0003345
> ***
> > treatment:hour 2 0.316 0.376 8 16 0.9183275
> > gender:hour 1 0.339 0.898 4 7 0.5129764
> > treatment:gender:hour 2 0.570 0.798 8 16 0.6131884
> > phase:hour 1 0.560 0.478 8 3 0.8202673
> > treatment:phase:hour 2 0.662 0.248 16 8 0.9915531
> > gender:phase:hour 1 0.712 0.925 8 3 0.5894907
> > treatment:gender:phase:hour 2 0.793 0.328 16 8 0.9723693
> > ---
> > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> >
> >
> >
> > #---code end
> >
> > #---code end
> >
> > #---code end
> >
> >
> >
> > Thanks in advance for your help!
> > Tal Galili
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > On Sun, Jan 25, 2009 at 3:08 AM, John Fox <j...@mcmaster.ca> wrote:
> >
> >
> > Dear Peter and Nils,
> >
> > In my initial message, I stated misleadingly that the contrast
> coding
> > didn't
> > matter for the "type-III" tests here since there is just one
> > between-subjects factor, but that's not right: The between type-III
> SS
> > is
> > correct using contr.treatment(), but the within SS is not. As is
> > generally
> > the case, to get reasonable type-III tests (i.e., tests of
> reasonable
> > hypotheses), it's necessary to have contrasts that are orthogonal
> in
> > the
> > row-basis of the design, such as contr.sum(), contr.helmert(), or
> > contr.poly(). The "type-II" tests, however, are insensitive to the
> > contrast
> > parametrization. Anova() always uses an orthogonal parametrization
> for
> > the
> > within-subjects design.
> >
> > The general advice in ?Anova is, "Be very careful in formulating
> the
> > model
> > for type-III tests, or the hypotheses tested will not make sense."
> >
> > Thanks, Peter, for pointing this out.
> >
> >
> > John
> >
> > ------------------------------
> > John Fox, Professor
> > Department of Sociology
> > McMaster University
> > Hamilton, Ontario, Canada
> > web: socserv.mcmaster.ca/jfox
> >
> >
> > > -----Original Message-----
> >
> > > From: Peter Dalgaard [mailto:p.dalga...@biostat.ku.dk]
> > > Sent: January-24-09 6:31 PM
> > > To: Nils Skotara
> > > Cc: John Fox; r-help@r-project.org; 'Michael Friendly'
> > > Subject: Re: [R] Anova and unbalanced designs
> > >
> >
> > > Nils Skotara wrote:
> > > > Dear John,
> > > >
> > > > thank you again! You replicated the type III result I got in
> SPSS!
> > When
> > I
> > > > calculate Anova() type II:
> > > >
> > > > Univariate Type II Repeated-Measures ANOVA Assuming Sphericity
> > > >
> > > > SS num Df Error SS den Df F Pr(>F)
> > > > between 4.8000 1 9.0000 8 4.2667 0.07273 .
> > > > within 0.2000 1 10.6667 8 0.1500 0.70864
> > > > between:within 2.1333 1 10.6667 8 1.6000 0.24150
> > > > ---
> > > > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> > > >
> > > > I see the exact same values as you had written.
> > > > However, and now I am really lost, type III (I did not change
> > anything
> > > else)
> > > > leads to the following:
> > > >
> > > > Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
> > > >
> > > > SS num Df Error SS den Df F
> > Pr(>F)
> > > > (Intercept) 72.000 1 9.000 8 64.0000
> > 4.367e-05
> > > ***
> > > > between 4.800 1 9.000 8 4.2667
> > 0.07273 .
> > > > as.factor(within) 2.000 1 10.667 8 1.5000
> > 0.25551
> > > > between:as.factor(within) 2.133 1 10.667 8 1.6000
> > 0.24150
> > > > ---
> > > > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> > > >
> > > > How is this possible?
> > >
> > > This looks like a contrast parametrization issue: If we look at
> the
> > > per-group mean within-differences and their SE, we get
> > >
> > > > summary(lm(within1-within2~between - 1))
> > > ..
> > > Coefficients:
> > > Estimate Std. Error t value Pr(>|t|)
> > > between1 -1.0000 0.8165 -1.225 0.256
> > > between2 0.3333 0.6667 0.500 0.631
> > > ..
> > > > table(between)
> > > between
> > > 1 2
> > > 4 6
> > >
> > > Now, the type II F test is based on weighting the two means as
> you
> > would
> > > after testing for no interaction
> > >
> > > > (4*-1+6*.3333)^2/(4^2*0.8165^2+6^2*0.6667^2)
> > > [1] 0.1500205
> > >
> > > and type III is to weight them as if there had been equal counts
> > >
> > > > (5*-1+5*.3333)^2/(5^2*0.8165^2+5^2*0.6667^2)
> > > [1] 0.400022
> > >
> > > However, the result above corresponds to looking at group1 only
> > >
> > > > (-1)^2/(0.8165^2)
> > > [1] 1.499987
> > >
> > > It helps if you choose orhtogonal contrast parametrizations:
> > >
> > > > options(contrasts=c("contr.sum","contr.helmert"))
> > > > betweenanova <- lm(values ~ between)> Anova(betweenanova,
> > idata=with,
> > > idesign= ~as.factor(within), type = "III" )
> > >
> > > Type III Repeated Measures MANOVA Tests: Pillai test statistic
> > > Df test stat approx F num Df den Df
> > Pr(>F)
> > > (Intercept) 1 0.963 209.067 1 8
> 5.121e-
> > 07
> > ***
> > > between 1 0.348 4.267 1 8
> > 0.07273 .
> > > as.factor(within) 1 0.048 0.400 1 8
> > 0.54474
> > > between:as.factor(within) 1 0.167 1.600 1 8
> > 0.24150
> > > ---
> > > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> > >
> > >
> > >
> > >
> > > --
> > > 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
> > > ~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45)
> > 35327907
> >
> > ______________________________________________
> > 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.
> >
> >
> >
> >
> >
> > --
> > ----------------------------------------------
> >
> >
> > My contact information:
> > Tal Galili
> > Phone number: 972-50-3373767
> > FaceBook: Tal Galili
> > My Blogs:
> > www.talgalili.com
> > www.biostatistics.co.il
> >
> >
>
>
>
>
--
----------------------------------------------
My contact information:
Tal Galili
Phone number: 972-50-3373767
FaceBook: Tal Galili
My Blogs:
www.talgalili.com
www.biostatistics.co.il
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