If you test multiple hypotheses at once, then you risk getting false positives. One way of dealing with this problem is by adjusting the p-values of your results to account for the fact that you’ve run multiple hypotheses. This package takes a vector of p-values, and outputs a vector of ‘’q-values’‘, in the style of Anderson (2008). Rejecting q-values below a threshold, say alpha = 0.05, will control for the’‘false-discovery rate’’ (FDR). In other words, the probability that a given significant result is a false positive will then be equal to or less than 0.05.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("dmbwebb/qval")This is a basic example which shows you how to solve a common problem:
library(qval)
set.seed(12345)
pval_example <- c(runif(10, 0, 1), runif(2, 0, 0.05))
pval_example
#> [1] 0.720903896 0.875773193 0.760982328 0.886124566 0.456480960 0.166371785
#> [7] 0.325095387 0.509224336 0.727705254 0.989736938 0.001726772 0.007618675
q_val(pval_example)
#> [1] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.022 0.044