# Which confidence interval adjustment should I do when using FDR p valures adjustment?

I need to do multiple comparison, and I want adjust the p-values by false discovery rate (fdr). However, it is impossible also adjust the confidence intervals by fdr.

What should I do? LSmeans by default gives bonferroni correction. Is it "consistent"(reasonable) to inform p values adjustment by fdr and confidence intervals by bonferroni for a researcher? (Or even none?)

m1 <- lm(Sepal.Length ~ Species, iris)
l1 <- lsmeans::lsmeans(m1, "Species")
summary(pairs(l1), infer = c(TRUE,TRUE), adjust = "fdr")
contrast               estimate      SE  df lower.CL upper.CL t.ratio p.value
setosa - versicolor      -0.930 0.10296 147 -1.17933 -0.68067  -9.033  <.0001
setosa - virginica       -1.582 0.10296 147 -1.83133 -1.33267 -15.366  <.0001
versicolor - virginica   -0.652 0.10296 147 -0.90133 -0.40267  -6.333  <.0001

Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 3 estimates
P value adjustment: fdr method for 3 tests


In this case, "fdr" and "bonferroni" give the same results, however, when you have more than 3 categories the results start to be different.