DearBeggMonotonePvalSelection: Compute simulation-based p-value to assess null hypothesis of...

In selectMeta: Estimation of Weight Functions in Meta Analysis

Compute simulation-based p-value to assess null hypothesis of no selection

Description

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).

Usage

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

Arguments

y	Normally distributed effect sizes.
u	Associated standard errors.
theta0	Initial estimate for \theta.
sigma0	Initial estimate for \sigma.
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.

Value

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.

Author(s)

Kaspar Rufibach (maintainer), [e-mail],
http://www.kasparrufibach.ch

References

Rufibach, K. (2011). Selection Models with Monotone Weight Functions in Meta-Analysis. Biom. J., 53(4), 689–704.

See Also

This function is illustrated in the help file for DearBegg.

selectMeta documentation built on April 11, 2025, 5:43 p.m.