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