adjusted p-values with TukeyHSD?

From: Sander Oom

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/m
ultiplecomp.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 <buser at stat.math.ethz.ch>
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 

didn??t find it on  

 > the list archive...
 > 
 > thx a lot, list, Chris
 > 
 > 
 > Christoph Strehblow, MD
 > Department of Rheumatology, Diabetes and Endocrinology
 > Wilhelminenspital, Vienna, Austria
 > chrisxe at gmx.at
 > 
 > ______________________________________________
 > R-help at stat.math.ethz.ch mailing list
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 > PLEASE do read the posting guide!