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

1,606

Version

1.3.0

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

January 7th, 2023

Functions in metaSEM (1.3.0)

A Dataset from Bornmann et al. (2007)

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

Correlation Matrices from Boer et al. (2016)

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

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Five Published Trails from Berkey et al. (1998)

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)

Ten Studies of Correlation Matrices used by Becker (2009)

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

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

Matrix Diagonals

Two Datasets from Gleser and Olkin (1994)

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

Simulated Effect Sizes Reported by Hox (2002)

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

Selected effect sizes from Cooper et al. (2003)

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

Correlation Matrices from Cooke et al. (2016)

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

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

Eight studies from Mak et al. (2009)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Correlation Matrices from Nohe et al. (2015)

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Correlation Matrices from Mathieu et al. (2015)

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

Dataset from Jaramillo, Mulki & Marshall (2005)

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

Extract Variance-Covariance Matrix of the Random Effects

Create a Block Diagonal Matrix

Convert a Matrix into MxMatrix-class

Compare Nested Models with Likelihood Ratio Statistic

Correlation coefficients reported by Tenenbaum and Leaper (2002)

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

Create a Block Diagonal Matrix by Repeating the Input

Correlations from Stadler et al. (2015)

Correlation Matrices from Scalco et al. (2017)

Convert a Character Matrix into MxAlgebra-class

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Fit Models on the bootstrapped correlation matrices

Create an F matrix to select observed variables

Extract Parameter Estimates from various classes.

Calculate Effect Sizes using lavaan Models

create.mxMatrix

Create a Vector into MxMatrix-class

Parametric bootstrap on the univariate R (uniR) object

create.modMatrix

Create a moderator matrix used in OSMASEM

Create a V-known matrix

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

Create a variance component of the heterogeneity of the random effects

Test Positive Definiteness of a List of Square Matrices

Check the correctness of the RAM formulation

Test the Homogeneity of Effect Sizes

A Dataset from Cheung and Chan (2005; 2009)

Create an mxModel

A Dataset from ISSP (2005)

Create or Generate the Model Implied Correlation or Covariance Matrices

Create a model implied correlation matrix with implicit diagonal constraints

Convert a List of Symmetric Matrices into a Stacked Matrix

Convert lavaan models to RAM models

Convert a Matrix into a Block Diagonal Matrix

Calculate the R2 in OSMASEM and OSMASEM3L

Calculate the SRMR in OSMASEM and OSMASEM3L

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

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

Display the Accumulative Sample Sizes for the Covariance Matrix

metaSEM-package

Meta-Analysis using Structural Equation Modeling

One-stage meta-analytic structural equation modeling

Convert metaSEM objects into semPlotModel objects for plotting

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

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

Generate (Nested) Sample/Population Correlation/Covariance Matrices

Compute Effect Sizes for Multiple Treatment Studies

Read External Correlation/Covariance Matrices

Compute Effect Sizes for Multiple End-point Studies

Plot methods for various objects

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

Rerun models via mxTryHard()

Print Methods for various Objects

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

Forty-four Studies from Cheung (2013)

Second Stage analysis of the univariate R (uniR) approach

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

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

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

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

Convert a Vector into a Symmetric Matrix

Conduct a Correlation/Covariance Structure Analysis with WLS

First Stage analysis of the univariate R (uniR) approach