weight_continuous: Weight for the continuous effect sizes
In mshasan/OPWeight: Optimal p-value weighting with independent information

Description Usage Arguments Details Value Author(s) See Also Examples

Compute weight from the probability of the rank given the effect size for the continuous effect size

1	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 `delta` values of a sequence. Note that, `delta` is a LaGrange multiplier, necessary to normalize the weight
`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 covariate 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 covariate 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)