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On Wed, 2 Aug 2006, Spencer Graves wrote:
> I'm not familiar with the correlation adjustment to Bonferroni you
> mention below, though it sounds interesting. However, I think there is
> something not right about it or about how you have interpreted it. Your code
> produced the following for me:
>
> p.value.raw p.value.bon p.value.adj
> = raw.p = bon.p =multcomp.p "bon.cor.p"
> diff/v=0 0.028572509 0.057145019 0.054951102 0.034934913
> diff/v=1 0.001727993 0.003455987 0.003415545 0.002119276
>
> In the absence of other information, I'd be inclined to believe
> csimint(..)$p.value.adj or ..$p.value.bon over your "bon.cor.p".
>
hm, I recall that we had some discussions if multiple comparisons
for fixed effects (as performed by Dominik) using
the `multcomp' functionality are admissible and none of the experts was
really sure about that -- and I was not able to find any helpful reference
when I looked into that problem two or three years ago. And thats the
reason why a simint method for lme objects is still missing ...
Best wishes,
Torsten
> Hope this helps.
> Spencer Graves
>
> Grathwohl, Dominik, LAUSANNE, NRC-BAS wrote:
>> Dear R-helpers:
>>
>> My question is how do I efficient and valid correct for multiple tests in a
>> repeated measurement design: Suppose we measure at two distinct visits with
>> repeated subjects a treatment difference on the same variable. The
>> treatment differences are assessed with a mixed model and adjusted by two
>> methods for multiple tests:
>>
>> # 1. Method: Adjustment with library(multcomp)
>>
>> library(nlme)
>> library(multcomp)
>>
>> n <- 30 # number of subjects
>> sd1 <- 0.5 # Standard deviation of the random intercept
>> sd2 <- 0.8 # Standard deviation of the residuals
>> id <- rep(1:n,times=2); v <- rep(0:1, each=n); trt <- rep(sample(rep(0:1,
>> each=n/2), n), times=2)
>> df <- data.frame(id, v, trt, y=2 + rep(rnorm(10,0,sd1), times=2) + 0.5*v +
>> 0.7*trt + 0.2*v*trt + rnorm(2*n, 0, sd2))
>> m1 <- lme(y ~ v + trt + v*trt, data=df, random= ~ 1|id)
>> summary(m1)
>> par4 <- m1$coef$fixed
>> cov4 <- vcov(m1)
>> cm4 <- matrix(c(0, 0, 1, 0, 0, 0, 1, 1), nrow = 2, ncol=4, byrow=TRUE,
>> dimnames = list(c("diff/v=0", "diff/v=1"), c("C.1", "C.2", "C.3", "C.4")))
>> v4 <- csimint(estpar=par4, df=n-6, # I'm not sure whether I found #
>> the correct degrees of freedom
>> covm=cov4,
>> cmatrix=cm4, conf.level=0.95)
>> sv4 <- summary(v4)
>>
>> # 2. Method: I found in Handbook of Statistics Vol 13, p.616,
>> # same can be found in http://home.clara.net/sisa/bonhlp.htm
>> # Bonferroni on correlated outcomes:
>>
>> raw.p <- sv4$p.value.raw
>> co4 <- cor(df$y[df$v==0],df$y[df$v==1])
>> rho <- mean(c(1,co4,co4,1))
>> pai <- 1-(1-raw.p)^2^(1-rho)
>> # The results of two methods are presented in the following lines:
>> out <- cbind(raw.p, sv4$p.value.bon, sv4$p.value.adj, pai)
>> colnames(out) <- c("raw.p", "bon.p", "multcomp.p", "bon.cor.p")
>> out
>>
>> As you can see there are quite big differences between the two ways
>> adjusting for multiple tests on repeated measurements. I guess that the
>> multcomp library is not appropriate for this kind of hypotheses. However I
>> could not find an explanation in the help files. May be one of the experts
>> can point me in the right direction?
>>
>> Kind regards,
>>
>> Dominik
>>
>> 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
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
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