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")

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Install

install.packages('metaSEM')

Monthly Downloads

973

Version

1.0.0

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

January 13th, 2018

Functions in metaSEM (1.0.0)

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

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

Five Published Trails from Berkey et al. (1998)

Correlation Matrices from Boer et al. (2016)

A Dataset from Bornmann et al. (2007)

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

Ten Studies of Correlation Matrices used by Becker (2009)

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

Selected effect sizes from Cooper et al. (2003)

Matrix Diagonals

Dataset from Jaramillo, Mulki & Marshall (2005)

Eight studies from Mak et al. (2009)

Simulated Effect Sizes Reported by Hox (2002)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Create a Block Diagonal Matrix by Repeating the Input

Parametric bootstrap on the univariate R (uniR) object

Test the Homogeneity of Effect Sizes

Create or Generate the Model Implied Correlation or Covariance Matrices

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

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Compare Nested Models with Likelihood Ratio Statistic

Convert a Matrix into MxMatrix-class

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

Test Positive Definiteness of a List of Square Matrices

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

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

Extract Variance-Covariance Matrix of the Random Effects

Read External Correlation/Covariance Matrices

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Plot method for meta objects

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

Rerun models via mxTryHard()

Convert a Vector into a Symmetric Matrix

Conduct a Correlation/Covariance Structure Analysis with WLS

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

Correlation Matrices from Cooke et al. (2016)

Correlation Matrices from Nohe et al. (2015)

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

Fit Models on the bootstrapped correlation matrices

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

Convert metaSEM objects into semPlotModel objects for plotting

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Compute Effect Sizes for Multiple End-point Studies

Compute Effect Sizes for Multiple Treatment Studies

Estimate the heterogeneity (SD) of the parameter estimates of the TSSEM object

Create an F matrix to select observed variables

create.mxMatrix

Create a Vector into MxMatrix-class

Convert lavaan models to RAM models

Convert a List of Symmetric Matrices into a Stacked Matrix

Summary Method for tssem1, wls, meta and meta3X Objects

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

First Stage analysis of the univariate R (uniR) approach

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

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

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create a Block Diagonal Matrix

A Dataset from ISSP (2005)

Convert a Matrix into a Block Diagonal Matrix

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Print Methods for various Objects

A Dataset from Cheung and Chan (2005; 2009)

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Display the Accumulative Sample Sizes for the Covariance Matrix

Second Stage analysis of the univariate R (uniR) approach

Extract Covariance Matrix Parameter Estimates from Various Objects

Generate Sample/Population Correlation/Covariance Matrices

Forty-four Studies from Cheung (2013)

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