num.true <- 9000
num.false <- 1000
num <- num.true + num.false

non.diff.mean <- 0
diff.mean <- 5


means <- c(rep(non.diff.mean,num.true),rep(diff.mean,num.false))

nsamples <- 5

Condition2 <- matrix(rnorm(nsamples*num,rep(means,nsamples)),num,nsamples)

Condition2.t <- apply(Condition2,1,mean)/sqrt(apply(Condition2,1,var)/nsamples)

Condition2.pval <- (1-pt(Condition2.t,nsamples))


FalsePositives <- sum(Condition2.pval[1:num.true] < 0.05)
FalseNegatives <- sum(Condition2.pval[(num.true+1):num] > 0.05)
FalsePositives;FalseNegatives


FalsePostivesBonferroni <- sum(Condition2.pval[1:num.true] < 0.05/num)
FalseNegativesBonferroni <- sum(Condition2.pval[(num.true+1):num] > 0.05/num)
FalsePostivesBonferroni;FalseNegativesBonferroni


FDR <- 0.05
cutoff <- max(sort(Condition2.pval)[sort(Condition2.pval) <=  (1:num)/num*FDR])
FalsePositivesFDR <- sum(Condition2.pval[1:num.true] < cutoff)
FalseNegativesFDR <- sum(Condition2.pval[(num.true+1):num] > cutoff)
FalsePositivesFDR;FalseNegativesFDR