prob_rank_givenEffect {OPWeight} | R Documentation |
Comnpute the probability of rank of a test being higher than any other tests given the effect size from external information.
prob_rank_givenEffect(k, et, ey, nrep = 10000, m0, m1)
k |
Integer, rank of a test |
et |
Numeric, effect of the targeted test for importance sampling |
ey |
Numeric, mean filter efffect from the external information |
nrep |
Integer, number of replications for importance sampling |
m0 |
Integer, number of true null hypothesis |
m1 |
Integer, number of true alternative hypothesis |
If one wants to test
H_0: epsilon_i=0 vs. H_a: epsilon_i > 0,
then ey
should be mean of the filter effect sizes,
This is called hypothesis testing for the continuous effect sizes.
If one wants to test
H_0: epsilon_i=0 vs. H_a: epsilon_i = epsilon,
then ey
should be median or any discrete value of the
filter effect sizes. This is called hypothesis testing for the Binary
effect sizes.
If monitor = TRUE
then a window will open to see the progress of the
computation. It is useful for a large number of tests
m1
and m0
can be estimated using qvalue
from
a bioconductor package qvalue
.
prob
Numeric, probability of the rank of a test
Mohamad S. Hasan, shakilmohamad7@gmail.com
# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000, m0 = 50, m1 = 50) # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks,prob)