effectBias: Compute bias for each effect size based on estimated weight...

In selectMeta: Estimation of Weight Functions in Meta Analysis

Description

Based on the estimated weight function an explicit formula for the bias of each initial effect estimate can be derived, see Rufibach (2011). This function implements computation of this bias and is called by DearBegg and DearBeggMonotone.

Usage

effectBias(y, u, w, theta, eta)

Arguments

y	Normally distributed effect sizes.
u	Associated standard errors.
w	Vector of estimated weights as computed by either DearBegg or DearBeggMonotone.
theta	Effect size estimate.
eta	Standard error of effect size estimate.

Value

A list consisting of the following elements:

dat	Matrix with columns y, u, y, p, bias, y - bias, bias / y, where the rows are provided in decreasing order of 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.

Examples

# For an illustration see the help file for the function DearBegg().

selectMeta documentation built on May 2, 2019, 4:22 a.m.