weight_continuous {OPWeight} | R Documentation |
Compute weight from the probability of the rank given the effect size for the continuous effect size
weight_continuous(alpha, et, m, tail = 1L, delInterval = 0.001, ranksProb)
alpha |
Numeric, significance level of the hypothesis test |
et |
Numeric, mean effect size of the test statistics |
m |
Integer, totoal number of hypothesis test |
tail |
Integer (1 or 2), right-tailed or two-tailed hypothesis test. default is right-tailed test. |
delInterval |
Numeric, interval between the |
ranksProb |
Numeric vector of ranks probability of the tests given the effect size |
If one wants to test
H_0: epsilon_i = 0 vs. H_a: ε_i > 0,
then et
and ey
should be mean value of the test and filter
effect sizes, respectively. This is called hypothesis testing for the continuous
effect sizes.
weight
Numeric vector of normalized weight of the tests
for the continuous case
Mohamad S. Hasan, shakilmohamad7@gmail.com
prob_rank_givenEffect
weight_binary
# 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 prob2 <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot the prooabbility plot(ranks, prob2) # compute weight for the continuous case weight_cont <- weight_continuous(alpha = .05, et = 1, m = 100, tail = 1, delInterval = .0001, ranksProb = prob2) # plot the weight plot(ranks, weight_cont)