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". 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]] > > ______________________________________________ > 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 > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ 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 and provide commented, minimal, self-contained, reproducible code.