Nothing
test.gMCP.correlation <- function() {
Gm <- matrix(0,nr=4,nc=4)
Gm[1,3] <- 1
Gm[2,4] <- 1
Gm[3,2] <- 1
Gm[4,1] <- 1
w <- c(1/2,1/2,0,0)
G <- matrix2graph(Gm,w)
Cm <- matrix(NA,nr=4,nc=4)
diag(Cm) <- 1
Cm[1,2] <- 1/2
Cm[2,1] <- 1/2
Cm[3,4] <- 1/2
Cm[4,3] <- 1/2
p <- c(0.0131,0.1,0.012,0.01)
x <- unname(gMCP(G,p,corr=Cm,test="Bretz2011",alpha=0.025)@rejected)
checkEquals(c(TRUE, FALSE, TRUE, FALSE), x)
}
test.gMCP.correlation.Alpha.Simulation <- function() {
if (!gMCP:::tests("extended")) {
cat("Skipping alpha level simulation.\n")
return()
}
N <- 100000
sigma <- matrix(0.9, nrow=3, ncol=3)
diag(sigma) <- 1
w <- c(0.4,0.4,0.2)
G <- matrix(1, nrow=3, ncol=3)
diag(G) <- 0
graph <- matrix2graph(G,w)
i <- 0
for(k in 1:N){
# simulate data with n obs and m-endpts
y=rmvnorm(1,mean=rep(0,3),sigma=sigma)
p <- as.numeric(pnorm(y))
res=gMCP(graph,p,corr=sigma,alpha=0.05,test="Bretz2011")
if(min(res@adjPValues)<=0.05){i=i+1}
}
eps <- 0.1
cat("Family-wise error is ",i/N, ".\n", sep="")
checkTrue(0.05-eps<i/N && i/N<0.05+eps)
}
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