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metaSEM (version 0.9.4)

Meta-Analysis using Structural Equation Modeling

Description

A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' package. It also implements the two-stage SEM approach to conduct meta-analytic structural equation modeling on correlation and covariance matrices.

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Install

install.packages('metaSEM')

Monthly Downloads

820

Version

0.9.4

License

GPL (>= 2)

Issues

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

June 8th, 2015

Functions in metaSEM (0.9.4)

Create a Block Diagonal Matrix by Repeating the Input

Effects of Open Education Reported by Hedges and Olkin (1985)

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

First Stage of the Two-Stage Structural Equation Modeling (TSSEM)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Data Set from Cheung and Chan (2005; 2009)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

create.mxMatrix

Create a Vector into MxMatrix-class

Six Studies of Correlation Matrices reported by Becker (1992; 1995)

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create an F matrix to select observed variables

Read External Correlation/Covariance Matrices

Convert a Vector into a Symmetric Matrix

Estimate Variance Components in Three-Level Univariate Meta-Analysis with Restricted (Residual) Maximum Likelihood Estimation

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Convert metaSEM objects into semPlotModel objects for plotting

Conduct a Correlation/Covariance Structure Analysis with WLS

Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy

Multivariate effect sizes between classroom management self-efficacy (CMSE) and other variables reported by Aloe et al. (2014)

Convert a List of Symmetric Matrices into a Stacked Matrix

Extract Parameter Estimates from tssem1FEM, tssem1FEM.cluster, tssem1REM, wls, wls.cluster, meta, meta3X, reml and MxRAMModel Objects

Dataset from Jaramillo, Mulki & Marshall (2005)

Five Published Trails from Berkey et al. (1998)

Factor Correlation Matrices of Big Five Model from Digman (1997)

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Estimate the asymptotic covariance matrix of standardized or unstandardized indirect and direct effects

Convert a Matrix into MxMatrix-class

Print Methods for tssem1FEM, tssem1FEM.cluster, tssem1REM, wls, meta, meta3X and reml Objects

Eight studies from Mak et al. (2009)

Create a Block Diagonal Matrix

Compare Nested Models with Likelihood Ratio Statistic

Convert a Matrix into a Block Diagonal Matrix

Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994)

Forty-four Studies from Cheung (2013)

Selected effect sizes from Cooper et al. (2003)

Test the Homogeneity of Effect Sizes

Plot method for meta objects

Rerun models via mxRun()

Studies on Sex Differences in Conformity Reported by Becker (1983)

Display the Accumulative Sample Sizes for the Covariance Matrix

Ten Studies of Correlation Matrices used by Becker (2009)

Test Positive Definiteness of a List of Square Matrices

Fifty Studies of Correlation Matrices used in Cheung and Chan (2000)

Simulated Effect Sizes Reported by Hox (2002)

A Data Set from TSSEM User's Guide Version 1.11 by Cheung (2009)

Matrix Diagonals

A Dataset from Bornmann et al. (2007)

Summary Method for tssem1, wls, meta and meta3X Objects

Data Set from ISSP (2005)

Display the Pattern of Missing Data of a List of Square Matrices

Extract Covariance Matrix Parameter Estimates from Various Objects