DearBeggMonotonePvalSelection: Compute simulation-based p-value to assess null hypothesis of...
In selectMeta: Estimation of Weight Functions in Meta Analysis

This function computes a simulation-based p-value to assess the null hypothesis of no selection. For details we refer to Rufibach (2011, Section 6).

1 2	DearBeggMonotonePvalSelection(y, u, theta0, sigma0, lam = 2, M = 1000, maxiter = 1000, test.stat = function(x){return(min(x))})

`y`	Normally distributed effect sizes.
`u`	Associated standard errors.
`theta0`	Initial estimate for θ.
`sigma0`	Initial estimate for σ.
`lam`	Weight of the first entry of w in the likelihood function. Should be the same as used to generate `res`.
`M`	Number of runs to compute p-value.
`maxiter`	Maximum number of iterations of differential evolution algorithm. Increase this number to get higher accuracy.
`test.stat`	A function that takes as argument a vector and returns a number. Defines the test statistic to be used on the estimated selection function w.

`pval`	The computed p-value.
`res.mono`	The monotone estimates for each simulation run.
`mono0`	The monotone estimates for the original data.
`Ti`	The test statistics for each simulation run.
`T0`	The test statistic for the original data.
`ran.num`	Matrix that contains the generated p-values.