Hi Chris and Chris,

I was keeping my eye on this thread as I have also been discovering multiple comparisons recently. Your instructions are very clear! Thanks.

Now I would love to see an R boffin write a nifty function to produce a graphical representation of the multiple comparison, like this one:

http://www.theses.ulaval.ca/2003/21026/21026024.jpg

Should not be too difficult.....[any one up for the challenge?]

I came across more multiple comparison info here;

http://www.agr.kuleuven.ac.be/vakken/statisticsbyR/ANOVAbyRr/multiplecomp.htm

Cheers,

Sander.

Christoph Buser wrote:
Dear Christoph

You can use the multcomp package. Please have a look at the
following example:

library(multcomp)

The first two lines were already proposed by Erin Hodgess:

summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks))
TukeyHSD(fm1, "tension", ordered = TRUE)

Tukey multiple comparisons of means
95% family-wise confidence level
factor levels have been ordered
Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks)


$tension
diff lwr upr
M-H 4.722222 -4.6311985 14.07564
L-H 14.722222 5.3688015 24.07564
L-M 10.000000 0.6465793 19.35342


By using the functions simtest or simint you can get the
p-values, too:

summary(simtest(breaks ~ wool + tension, data = warpbreaks, whichf="tension",
        type = "Tukey"))

Simultaneous tests: Tukey contrasts

Call: simtest.formula(formula = breaks ~ wool + tension, data = warpbreaks, whichf = "tension", type = "Tukey")

Tukey contrasts for factor tension, covariable: wool

Contrast matrix:
                      tensionL tensionM tensionH
tensionM-tensionL 0 0       -1        1        0
tensionH-tensionL 0 0       -1        0        1
tensionH-tensionM 0 0        0       -1        1


Absolute Error Tolerance: 0.001


Coefficients:
                  Estimate t value Std.Err. p raw p Bonf p adj
tensionH-tensionL  -14.722  -3.802    3.872 0.000  0.001 0.001
tensionM-tensionL  -10.000  -2.582    3.872 0.013  0.026 0.024
tensionH-tensionM   -4.722  -1.219    3.872 0.228  0.228 0.228



or if you prefer to get the confidence intervals, too, you can
use:

summary(simint(breaks ~ wool + tension, data = warpbreaks, whichf="tension",
        type = "Tukey"))

        Simultaneous 95% confidence intervals: Tukey contrasts

Call: simint.formula(formula = breaks ~ wool + tension, data = warpbreaks, whichf = "tension", type = "Tukey")

Tukey contrasts for factor tension, covariable: wool

Contrast matrix:
                      tensionL tensionM tensionH
tensionM-tensionL 0 0       -1        1        0
tensionH-tensionL 0 0       -1        0        1
tensionH-tensionM 0 0        0       -1        1

Absolute Error Tolerance: 0.001

95 % quantile: 2.415

Coefficients:
                  Estimate   2.5 % 97.5 % t value Std.Err. p raw p Bonf p adj
tensionM-tensionL  -10.000 -19.352 -0.648  -2.582    3.872 0.013  0.038 0.034
tensionH-tensionL  -14.722 -24.074 -5.370  -3.802    3.872 0.000  0.001 0.001
tensionH-tensionM   -4.722 -14.074  4.630  -1.219    3.872 0.228  0.685 0.447

-----------------------------------------------------------------
Please be careful: The resulting confidence intervals in
simint are not associated with the p-values from 'simtest' as it
is described in the help page of the two functions.
-----------------------------------------------------------------

I had not the time to check the differences in the function or
read the references given on the help page.
If you are interested in the function you can check those to
find out which one you prefer.

Best regards,

Christoph Buser

--------------------------------------------------------------
Christoph Buser <[EMAIL PROTECTED]>
Seminar fuer Statistik, LEO C13
ETH (Federal Inst. Technology)  8092 Zurich      SWITZERLAND
phone: x-41-44-632-4673         fax: 632-1228
http://stat.ethz.ch/~buser/
--------------------------------------------------------------


Christoph Strehblow writes:
> hi list,
> > i have to ask you again, having tried and searched for several days...
> > i want to do a TukeyHSD after an Anova, and want to get the adjusted > p-values after the Tukey Correction.
> i found the p.adjust function, but it can only correct for "holm", > "hochberg", bonferroni", but not "Tukey".
> > Is it not possbile to get adjusted p-values after Tukey-correction?
> > sorry, if this is an often-answered-question, but i didnīt find it on > the list archive...
> > thx a lot, list, Chris
> > > Christoph Strehblow, MD
> Department of Rheumatology, Diabetes and Endocrinology
> Wilhelminenspital, Vienna, Austria
> [EMAIL PROTECTED]
> > ______________________________________________
> 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


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--------------------------------------------
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Animal, Plant and Environmental Sciences,
University of the Witwatersrand
Private Bag 3, Wits 2050, South Africa
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