mle_meta: Computing the publication probability in meta analyses

In t-sager/pubias: Identification & Correction for Publication Bias

Description

mle_meta() calculates the publication probability, its variance and robust standard errors of replication studies by a Maximum Likelihood approach. Check package vignette for further information.

Usage

mle_meta(X, sigma, symmetric, cluster_ID, cutoffs, C)

Arguments

X	A n x 1 matrix containing the estimates, where n is the number of estimates.
sigma	A n x 1 matrix containing the standard errors of the estimates, where n is the number of estimates.
symmetric	If set to TRUE, the publication probability is assumed to be symmetric around zero. If set to FALSE, asymmetry is allowed.
cluster_ID	A n x 1 matrix containing IDs going from 1 to n, where n is the number of estimates.
cutoffs	A matrix containing the thresholds for the steps of the publication probability. Should be strictly increasing column vector of size k x 1 where k is the number of cutoffs.
C	A n x 1 matrix with all values being 1. Controls.

Value

Returns a list object with the publication probability (Psihat), its variance (Varhat) and robust standard error (se_robust).

t-sager/pubias documentation built on Dec. 23, 2021, 7:41 a.m.