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

1,606

Version

1.2.3.1

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

December 8th, 2019

Functions in metaSEM (1.2.3.1)

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

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

A Dataset from Bornmann et al. (2007)

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

Two Datasets from Gleser and Olkin (1994)

Correlation Matrices from Gnambs, Scharl, and Schroeders (2018)

Correlation Matrices from Nohe et al. (2015)

Correlation Matrices from Cooke et al. (2016)

Dataset on the Environmental Tobacco Smoke (ETS) on children's health

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

Selected effect sizes from Cooper et al. (2003)

Convert correlation or covariance matrices into a dataframe of correlations or covariances with their sampling covariance matrices

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

Eight studies from Mak et al. (2009)

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Compare Nested Models with Likelihood Ratio Statistic

Simulated Effect Sizes Reported by Hox (2002)

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Extract Variance-Covariance Matrix of the Random Effects

Convert a Matrix into MxMatrix-class

Matrix Diagonals

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Create a Block Diagonal Matrix

Dataset from Jaramillo, Mulki & Marshall (2005)

Create a model implied correlation matrix with implicit diagonal constraints

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create a variance component of the heterogeneity of the random effects

Test the Homogeneity of Effect Sizes

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

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

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

Correlation Matrices from Scalco et al. (2017)

Calculate the SRMR in OSMASEM

Create a V-known matrix

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Convert a List of Symmetric Matrices into a Stacked Matrix

Extract Parameter Estimates from various classes.

Create a Block Diagonal Matrix by Repeating the Input

Parametric bootstrap on the univariate R (uniR) object

Correlations from Stadler et al. (2015)

Fit Models on the bootstrapped correlation matrices

Check the correctness of the RAM formulation

A Dataset from ISSP (2005)

Create an mxModel

Calculate the R2 in OSMASEM

One-stage meta-analytic structural equation modeling

create.mxMatrix

Create a Vector into MxMatrix-class

Test Positive Definiteness of a List of Square Matrices

Rerun models via mxTryHard()

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

Convert a Matrix into a Block Diagonal Matrix

Create or Generate the Model Implied Correlation or Covariance Matrices

Read External Correlation/Covariance Matrices

Summary Method for tssem1, wls, meta, and meta3X Objects

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Create an F matrix to select observed variables

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Convert metaSEM objects into semPlotModel objects for plotting

Display the Accumulative Sample Sizes for the Covariance Matrix

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

Print Methods for various Objects

First Stage analysis of the univariate R (uniR) approach

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

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

Generate Sample/Population Correlation/Covariance Matrices

Compute Effect Sizes for Multiple End-point Studies

Compute Effect Sizes for Multiple Treatment Studies

A Dataset from Cheung and Chan (2005; 2009)

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

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

Convert lavaan models to RAM models

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

Convert a Vector into a Symmetric Matrix

Second Stage analysis of the univariate R (uniR) approach

Plot methods for various objects

Dataset on the effectiveness of multidimensional family therapy in treating adolescents with multiple behavior problems

Conduct a Correlation/Covariance Structure Analysis with WLS

Forty-four Studies from Cheung (2013)

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

Ten Studies of Correlation Matrices used by Becker (2009)

Five Published Trails from Berkey et al. (1998)

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

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Correlation Matrices from Boer et al. (2016)