metaSEM package - RDocumentation

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The metaSEM package conducts univariate and multivariate meta-analyses 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/covariance matrices.

The stable version can be installed from CRAN by:

install.packages("metaSEM")

The developmental version can be installed from GitHub by:

## Install devtools package if it has not been installed yet
# install.packages("devtools")

devtools::install_github("mikewlcheung/metasem")

Please refer to https://courses.nus.edu.sg/course/psycwlm/Internet/metaSEM/ for more detail.

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Install

install.packages('metaSEM')

Monthly Downloads

820

Version

0.9.10

License

GPL (>= 2)

Issues

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

August 18th, 2016

Functions in metaSEM (0.9.10)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Convert a Matrix into MxMatrix-class

Ten Studies of Correlation Matrices used by Becker (2009)

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

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

Create a Block Diagonal Matrix by Repeating the Input

Compare Nested Models with Likelihood Ratio Statistic

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create a Block Diagonal Matrix

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

create.mxMatrix

Create a Vector into MxMatrix-class

Matrix Diagonals

Five Published Trails from Berkey et al. (1998)

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

Create an F matrix to select observed variables

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

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

Selected effect sizes from Cooper et al. (2003)

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

A Dataset from Bornmann et al. (2007)

Test the Homogeneity of Effect Sizes

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

Simulated Effect Sizes Reported by Hox (2002)

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

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

Data Set from Cheung and Chan (2005; 2009)

Data Set from ISSP (2005)

Test Positive Definiteness of a List of Square Matrices

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Create the Model Implied Correlation or Covariance Matrix

Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013)

Dataset from Jaramillo, Mulki & Marshall (2005)

Convert a List of Symmetric Matrices into a Stacked Matrix

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Display the Accumulative Sample Sizes for the Covariance Matrix

Eight studies from Mak et al. (2009)

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Convert lavaan models to RAM models

Convert a Matrix into a Block Diagonal Matrix

Convert metaSEM objects into semPlotModel objects for plotting

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

Plot method for meta objects

Summary Method for tssem1, wls, meta and meta3X Objects

Rerun models via mxTryHard()

Print Methods for various Objects

Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011)

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

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

Read External Correlation/Covariance Matrices

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

Extract Variance Covariance Matrix of the Random Effects

Convert a Vector into a Symmetric Matrix

Conduct a Correlation/Covariance Structure Analysis with WLS

Extract Covariance Matrix Parameter Estimates from Various Objects

Forty-four Studies from Cheung (2013)

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