selectMeta: Estimation of Weight Functions in Meta Analysis
Version 1.0.8

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).

Browse man pages Browse package API and functions Browse package files

AuthorKaspar Rufibach <kaspar.rufibach@gmail.com>
Date of publication2015-07-03 12:51:10
MaintainerKaspar Rufibach <kaspar.rufibach@gmail.com>
LicenseGPL (>= 2)
Version1.0.8
URL http://www.kasparrufibach.ch
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("selectMeta")

Man pages

DearBegg: Compute the nonparametric weight function from Dear and Begg...
DearBeggMonotoneCItheta: Compute an approximate profile likelihood ratio confidence...
DearBeggMonotonePvalSelection: Compute simulation-based p-value to assess null hypothesis of...
education: Dataset open vs. traditional education on creativity
effectBias: Compute bias for each effect size based on estimated weight...
IyenGreen: Compute MLE and weight functions of Iyengar and Greenhouse...
passive_smoking: Dataset on the effect of environmental tobacco smoke
pPool: Pool p-values in pairs
Pval: Functions for the distribution of p-values
selectMeta-package: Estimation of Weight Functions in Meta Analysis
weightLine: Function to plot estimated weight functions

Functions

DearBegg Man page Source code
DearBeggLoglik Man page Source code
DearBeggMonotone Man page Source code
DearBeggMonotoneCItheta Man page Source code
DearBeggMonotonePvalSelection Man page Source code
DearBeggProfileLL Man page Source code
DearBeggToMinimize Man page Source code
DearBeggToMinimizeProfile Man page Source code
Hij Man page Source code
IyenGreen Man page
IyenGreenLoglikT Man page Source code
IyenGreenMLE Man page Source code
IyenGreenWeight Man page Source code
Pval Man page
dPval Man page Source code
education Man page
effectBias Man page Source code
normalizeT Man page Source code
pPool Man page Source code
pPval Man page Source code
passive_smoking Man page
qPval Man page Source code
rPval Man page Source code
selectMeta Man page
selectMeta-package Man page
weightLine Man page Source code

Files

NAMESPACE
NEWS
data
data/education.rda
data/passive_smoking.rda
R
R/dPval.r
R/normalizeT.r
R/rPval.r
R/DearBeggMonotone.r
R/IyenGreenMLE.r
R/DearBeggMonotonePvalSelection.r
R/DearBeggProfileLL.r
R/DearBeggLoglik.r
R/pPool.r
R/DearBeggToMinimizeProfile.r
R/qPval.r
R/IyenGreenLoglikT.r
R/Hij.r
R/pPval.r
R/DearBeggToMinimize.r
R/weightLine.r
R/IyenGreenWeight.r
R/DearBegg.r
R/effectBias.r
R/DearBeggMonotoneCItheta.r
MD5
DESCRIPTION
man
man/Pval.Rd
man/weightLine.Rd
man/DearBeggMonotoneCItheta.Rd
man/DearBeggMonotonePvalSelection.Rd
man/education.Rd
man/effectBias.Rd
man/IyenGreen.Rd
man/passive_smoking.Rd
man/selectMeta-package.Rd
man/pPool.Rd
man/DearBegg.Rd
selectMeta documentation built on May 19, 2017, 3:37 p.m.