From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Sun 16 Jul 2006 - 01:32:42 EST

*>
*

> I do not believe that this is the most efficient way for doing this,

*> since I made already bad experience with the first example.
*

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 Received on Sun Jul 16 01:39:06 2006

Date: Sun 16 Jul 2006 - 01:32:42 EST

Grathwohl, Dominik, LAUSANNE, NRC-BAS wrote:

> Dear r-helpers,

*>
**> I have a question about multiple testing.
**> Here an example that puzzles me:
**> All matrixes and contrast vectors are presented in treatment contrasts.
**>
**> 1. example:
**> library(multcomp)
**> n<-60; sigma<-20
**> # n = sample size per group
**> # sigma standard deviation of the residuals
**>
**> cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE,
**> dimnames = list(c("A", "B", "C"), c("C.1", "C.2", "C.3")))
**> # cov1 = variance covariance matrix of the beta coefficients of a
**> # 2x2 factorial design (see Piantadosi 2005, p. 509)
**>
**> cm1 <- matrix(c(0, 1, 0, 0, 0, 1), nrow = 2, ncol=3, byrow=TRUE,
**> dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3")))
**> # cm1 = contrast matrix for main effects
**>
**> v1 <- csimint(estpar=c(100, 6, 5), df=4*n-3, covm=cov1*sigma^2/n, cmatrix=cm1, conf.level=0.95)
**> summary(v1)
**>
**> The adjusted p-values are almost the Bonferroni p-values.
**> If I understood right: You need not to adjust for multiple testing
**> on main effects in a 2x2 factorial design
**> assuming the absence of interaction.
*

SG: Where did you get this idea? A p value of 0.05 says that if the null hypothesis of no effect is true, a result at least as extreme as that observed work actually occur with probability 0.05. Thus, with 2 independent tests, the probability of getting a result at least that extreme in one or both of the tests is 1-(1-0.05)^2 = 0.0975, which is almost 2*0.05. Thus, if I were to consider only main effects in a 2x2 factorial design, this is what I would get from Bonferroni.

> I do not think that there is a bug,

*> I want to understand, why multcomp does adjust for multiple tests
**> having all information about the design of the trial (variance covariance matrix)?
**> Or do I have to introduce somehow more information?
**>
**> 2. example:
**> And I have second question: How do I proper correct for multiple testing
**> if I want to estimate in the presence of interaction the two average main effects.
**> Can some one point me to some literature where I can learn these things?
**> Here the example, 2x2 factorial with interaction, estimation of average main effects:
**>
**> cov2 <- matrix(
**> c(1,-1,-1, 1,
**> -1, 2, 1,-2,
**> -1, 1, 2,-2,
**> 1,-2,-2, 4)
**> , nrow=4, ncol=4, byrow=TRUE)
**> cm2 <- matrix(c(0, 1, 0, 1/2, 0, 0, 1, 1/2), nrow = 2, ncol=4, byrow=TRUE,
**> dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3", "C.4")))
**> v2 <- csimint(estpar=c(100, 6, 5, 2), df=4*n-4, covm=cov2*sigma^2/n, cmatrix=cm2, conf.level=0.95)
**> summary(v2)
*

SG: The Bonferroni p-value is the observed times the number of rows in the contrast matrix. The number of columns is irrelevant to Bonferroni.

SG: I'm not sure, but I believe that the the adjusted p value would likely be close to (if not exactly) the rank of the contrast matrix; given the rank, it is (I think) independent of the number of rows and columns.

SG: These two assertions are consistent with the following example, where I increase the number of dimensions by a factor of 4 without changing the rank. The Bonferroni p value increased by a factor of 4 while the adjusted p value did not change, as predicted.

cm2.4 <- rbind(cm2, cm2, cm2, cm2)

v2.4 <- csimint(estpar=c(100, 6, 5, 2), df=4*n-4,

covm=cov2*sigma^2/n, cmatrix=cm2.4, conf.level=0.95)summary(v2.4)

> I do not believe that this is the most efficient way for doing this,

SG: I hope this reply converts the "bad experience" to "good". As for efficiency, you did very well by including such simple but elegant examples. Your post might have more efficiently elicited more and more elegant responses sooner with a more carefully chosen Subject, perhaps like "Multiple Comparisons Questions". However, the selection of a possible better subject might rely on information you didn't have.

Hope this helps.

Spencer Graves

*>
*

> My R.version:

*>
**> platform i386-pc-mingw32
**> arch i386
**> os mingw32
**> system i386, mingw32
**> status
**> major 2
**> minor 2.1
**> year 2005
**> month 12
**> day 20
**> svn rev 36812
**> language R
**>
**> ______________________________________________
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**> 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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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sun Jul 16 01:39:06 2006

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