[R] adjusted p-values with TukeyHSD?

[Christoph Strehblow]

Hi!

Thanks a lot, works as advertised. If i used Tukey, it even gives  
raw, Bonferroni- and Tukey-corrected p-values!

Thx for the help,


Christoph Strehblow, MD
Department of Rheumatology, Diabetes and Endocrinology
Wilhelminenspital, Vienna, Austria
chrisxe at gmx.at


Am 17.05.2005 um 13:23 schrieb Christoph Buser:

> 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
>>
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>
>