weight_continuous_nwt: 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

This is a Newton Raphson based algorithm to compute weight from the ranks probability for the continuous effect sizes.

1	weight_continuous_nwt(alpha, et, ranksProb, x0 = NULL)

`alpha`	Numeric, significance level of the hypothesis test
`et`	Numeric, mean effect size of the test statistics
`ranksProb`	Numeric vector of ranks probability of the tests given the effect size
`x0`	Numeric, a initial value for the Newton-Raphson mehod.

If one wants to test

H_0: epsilon_i = 0 vs. H_a: ε_i > 0,

then et should be mean of the test effect sizes. This is called hypothesis testing for the continuous effect sizes.

For the Newton-Raphson mehthod the initial value x0 is very important. One may need to supply externally if the function shows error. Newton-Raphson method may not work in some cases. In that situations, use weight_continuous function, which is a general but slow approach to compute weights beased on the grid search algorithm.

w Numeric vector of weights of the tests for the continuous case

lambda Numeric value of the LaGrange multiplier

n Integer, number of iteration needed to obtain the initial value

k Integer, number of iteration needed to obtain the optimal value of lambda

Mohamad S. Hasan, shakilmohamad7@gmail.com

prob_rank_givenEffect weight_continuous

# 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
prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000,
                              m0 = 50, m1 = 50)

# compute weight for the continuous case
results = weight_continuous_nwt(alpha = .05, et = 2,
                                             ranksProb = prob)