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

remotes::install_github("mikewlcheung/metasem")

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Install

install.packages('metaSEM')

Monthly Downloads

901

Version

1.5.0

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

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Last Published

September 26th, 2024

Functions in metaSEM (1.5.0)

A Dataset 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)

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

Correlation Matrices from Cooke et al. (2016)

Two Datasets from Gleser and Olkin (1994)

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

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

Matrix Diagonals

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

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

Dataset from Jaramillo, Mulki and Marshall (2005)

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

Correlation Matrices from Nohe et al. (2015)

Eight studies from Mak et al. (2009)

Correlation Matrices from Mathieu et al. (2015)

Simulated Effect Sizes Reported by Hox (2002)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Compare Nested Models with Likelihood Ratio Statistic

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Create a Block Diagonal Matrix

Convert a Character Matrix into MxAlgebra-class

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

Convert a Matrix into MxMatrix-class

Create a V-known matrix

create.modMatrix

Create a moderator matrix used in OSMASEM

Create an F matrix to select observed variables

Create a variance component of the heterogeneity of the random effects

Parametric bootstrap on the univariate R (uniR) object

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Create a Block Diagonal Matrix by Repeating the Input

Correlation Matrices from Scalco et al. (2017)

Create a model implied correlation matrix with implicit diagonal constraints

Create or Generate the Model Implied Correlation or Covariance Matrices

Fit Models on the bootstrapped correlation matrices

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Correlations from Stadler et al. (2015)

Display the Accumulative Sample Sizes for the Covariance Matrix

create.mxMatrix

Create a Vector into MxMatrix-class

Extract Parameter Estimates from various classes.

Convert lavaan models to RAM models

Convert a List of Symmetric Matrices into a Stacked Matrix

Extract Variance-Covariance Matrix of the Random Effects

Test the Homogeneity of Effect Sizes

Calculate Effect Sizes using lavaan Models

Convert metaSEM objects into semPlotModel objects for plotting

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

Check the correctness of the RAM formulation

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

A Dataset from ISSP (2005)

Test Positive Definiteness of a List of Square Matrices

Compute Effect Sizes for Multiple End-point Studies

Calculate the R2 in OSMASEM and OSMASEM3L

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

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

Conduct a Correlation/Covariance Structure Analysis with WLS

Calculate the SRMR in OSMASEM and OSMASEM3L

Compute Effect Sizes for Multiple Treatment Studies

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

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

Convert a Vector into a Symmetric Matrix

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

A Dataset from Cheung and Chan (2005; 2009)

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Plot methods for various objects

Second Stage analysis of the univariate R (uniR) approach

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Forty-four Studies from Cheung (2013)

Fit a structural equation model using OpenMx

Convert a Matrix into a Block Diagonal Matrix

One-stage meta-analytic structural equation modeling

Rerun models via mxTryHard()

Summary Method for tssem1, wls, meta, and meta3LFIML Objects

Print Methods for various Objects

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

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

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

First Stage analysis of the univariate R (uniR) approach

Read External Correlation/Covariance Matrices

Generate (Nested) Sample/Population Correlation/Covariance Matrices

Correlation Matrices from Boer et al. (2016)

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)

Five Published Trails from Berkey et al. (1998)

Dataset from Chan, Jones, Jamieson, and Albarracin (2017)

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

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

A Dataset from Bornmann et al. (2007)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Ten Studies of Correlation Matrices used by Becker (2009)