Re: [R] adjusted p-values with TukeyHSD?
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 difflwr upr M-H 4.72 -4.6311985 14.07564 L-H 14.72 5.3688015 24.07564 L-M 10.00 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 Absolute Error Tolerance: 0.001 Coefficients: Estimate t value Std.Err. p raw p Bonf p adj tensionH-tensionL -14.722 -3.8023.872 0.000 0.001 0.001 tensionM-tensionL -10.000 -2.5823.872 0.013 0.026 0.024 tensionH-tensionM -4.722 -1.2193.872 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 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 -2.5823.872 0.013 0.038 0.034 tensionH-tensionL -14.722 -24.074 -5.370 -3.8023.872 0.000 0.001 0.001 tensionH-tensionM -4.722 -14.074 4.630 -1.2193.872 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 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 [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 __ 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
Re: [R] adjusted p-values with TukeyHSD?
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 [EMAIL PROTECTED] 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 difflwr upr M-H 4.72 -4.6311985 14.07564 L-H 14.72 5.3688015 24.07564 L-M 10.00 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 Absolute Error Tolerance: 0.001 Coefficients: Estimate t value Std.Err. p raw p Bonf p adj tensionH-tensionL -14.722 -3.8023.872 0.000 0.001 0.001 tensionM-tensionL -10.000 -2.5823.872 0.013 0.026 0.024 tensionH-tensionM -4.722 -1.2193.872 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 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 -2.5823.872 0.013 0.038 0.034 tensionH-tensionL -14.722 -24.074 -5.370 -3.8023.872 0.000 0.001 0.001 tensionH-tensionM -4.722 -14.074 4.630 -1.2193.872 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-41-44-632-4673fax: 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 [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 __ 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
Re: [R] adjusted p-values with TukeyHSD?
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 difflwr upr M-H 4.72 -4.6311985 14.07564 L-H 14.72 5.3688015 24.07564 L-M 10.00 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 Absolute Error Tolerance: 0.001 Coefficients: Estimate t value Std.Err. p raw p Bonf p adj tensionH-tensionL -14.722 -3.8023.872 0.000 0.001 0.001 tensionM-tensionL -10.000 -2.5823.872 0.013 0.026 0.024 tensionH-tensionM -4.722 -1.2193.872 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 -110 tensionH-tensionL 0 0 -101 tensionH-tensionM 0 00 -11 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 -2.5823.872 0.013 0.038 0.034 tensionH-tensionL -14.722 -24.074 -5.370 -3.8023.872 0.000 0.001 0.001 tensionH-tensionM -4.722 -14.074 4.630 -1.2193.872 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 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 [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 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read
Re: [R] adjusted p-values with TukeyHSD?
Shame I can not get hold of Hsu, J. C. and M. Peruggia (1994) just now.
I am quite curious to see what their graphs look like. Would you be able
to give an example in R.? ;-)
The graph I put forward is typically used by ecologists to summarize
data. It comes down to a simple means plot with error bars. Significant
differences of multiple comparisons are then added using the letters a,
b, c etc. If two bars have the same letter, they are not significantly
different. It can become quite complicated when mean one is different
from mean three but not from mean two and mean two is different from
mean three but not mean one. You then get: a, ab, c for mean one, two
and three respectively.
Of course what is often used does not constitute the best way of doing it.
Sander.
Liaw, Andy wrote:
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.
One thing to note, though: Multcomp does not do Dunnett's or
Tukey's multiple comparisons per se. Those names in multcomp
refer to the contrasts being used (comparison to a control for
Dunnett and all pairwise comparison for Tukey). The actual
methods used are as described in the references of the help
pages.
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 beg to differ: That's probably as bad a way as one can use to
graphically show multiple comparison. The shaded bars serve no
purpose.
Two alternatives that I'm aware of are
- Multiple comparison circles, due to John Sall, and not
surprisingly, implemented in JMP and SAS/Insight. See:
http://support.sas.com/documentation/onlinedoc/v7/whatsnew/insight/sect4.htm
- The mean-mean display proposed by Hsu and Peruggia:
Hsu, J. C. and M. Peruggia (1994).
Graphical representations of Tukey's multiple comparison method.
Journal of Computational and Graphical Statistics 3, 143{161
Andy
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
difflwr upr
M-H 4.72 -4.6311985 14.07564
L-H 14.72 5.3688015 24.07564
L-M 10.00 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 -110
tensionH-tensionL 0 0 -101
tensionH-tensionM 0 00 -11
Absolute Error Tolerance: 0.001
Coefficients:
Estimate t value Std.Err. p raw p Bonf p adj
tensionH-tensionL -14.722 -3.8023.872 0.000 0.001 0.001
tensionM-tensionL -10.000 -2.5823.872 0.013 0.026 0.024
tensionH-tensionM -4.722 -1.2193.872 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 -110
tensionH-tensionL 0 0 -101
tensionH-tensionM 0 00 -11
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 -2.5823.872
0.013 0.038 0.034
tensionH-tensionL -14.722 -24.074 -5.370 -3.8023.872
0.000 0.001 0.001
tensionH-tensionM -4.722 -14.074 4.630 -1.2193.872
0.228 0.685 0.447
-
Please be careful: The resulting confidence intervals in
simint are not associated with the p-values
