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.packages('metaSEM')

Monthly Downloads

1,010

Version

1.2.5.1

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

May 17th, 2021

Functions in metaSEM (1.2.5.1)

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

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

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

A Dataset from Bornmann et al. (2007)

Ten Studies of Correlation Matrices used by Becker (2009)

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

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

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

Matrix Diagonals

Five Published Trails from Berkey et al. (1998)

Correlation Matrices from Nohe et al. (2015)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Dataset from Jaramillo, Mulki & Marshall (2005)

Convert a Character Matrix with Starting Values to a Character Matrix without Starting Values

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

Correlation Matrices from Cooke et al. (2016)

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Two Datasets from Gleser and Olkin (1994)

Calculate Effect Sizes using lavaan Models

Check the correctness of the RAM formulation

Extract Variance-Covariance Matrix of the Random Effects

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

Convert lavaan models to RAM models

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

Correlation Matrices from Mathieu et al. (2015)

Compare Nested Models with Likelihood Ratio Statistic

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

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

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

Create a Block Diagonal Matrix

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Selected effect sizes from Cooper et al. (2003)

Correlation Matrices from Boer et al. (2016)

Create a Block Diagonal Matrix by Repeating the Input

Convert a List of Symmetric Matrices into a Stacked Matrix

create.modMatrix

Create a moderator matrix used in OSMASEM

Calculate the R2 in OSMASEM

Parametric bootstrap on the univariate R (uniR) object

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

Simulated Effect Sizes Reported by Hox (2002)

Test the Homogeneity of Effect Sizes

Fit Models on the bootstrapped correlation matrices

Create an F matrix to select observed variables

Extract Parameter Estimates from various classes.

Correlation Matrices from Scalco et al. (2017)

Correlations from Stadler et al. (2015)

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Create or Generate the Model Implied Correlation or Covariance Matrices

Calculate the SRMR in OSMASEM

create.mxMatrix

Create a Vector into MxMatrix-class

Forty-four Studies from Cheung (2013)

Generate Sample/Population Correlation/Covariance Matrices

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

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

Read External Correlation/Covariance Matrices

Test Positive Definiteness of a List of Square Matrices

Create an mxModel

Create a variance component of the heterogeneity of the random effects

Display the Accumulative Sample Sizes for the Covariance Matrix

Convert a Matrix into a Block Diagonal Matrix

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

A Dataset from ISSP (2005)

A Dataset from Cheung and Chan (2005; 2009)

Create a V-known matrix

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Compute Effect Sizes for Multiple End-point Studies

One-stage meta-analytic structural equation modeling

Rerun models via mxTryHard()

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

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

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

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

Compute Effect Sizes for Multiple Treatment Studies

Convert a Vector into a Symmetric Matrix

Conduct a Correlation/Covariance Structure Analysis with WLS

Create a model implied correlation matrix with implicit diagonal constraints

Plot methods for various objects

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Convert metaSEM objects into semPlotModel objects for plotting

Print Methods for various Objects

First Stage analysis of the univariate R (uniR) approach

Second Stage analysis of the univariate R (uniR) approach

Eight studies from Mak et al. (2009)

Convert a Matrix into MxMatrix-class

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Convert a Character Matrix into MxAlgebra-class

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

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

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes