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.packages('metaSEM')

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1,039

Version

1.4.0

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

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Last Published

May 16th, 2024

Functions in metaSEM (1.4.0)

Correlation Matrices from Boer et al. (2016)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Five Published Trails from Berkey et al. (1998)

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

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

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

Ten Studies of Correlation Matrices used by Becker (2009)

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)

A Dataset from Bornmann et al. (2007)

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

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

Selected effect sizes from Cooper et al. (2003)

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

Correlation Matrices from Cooke et al. (2016)

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

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

Two Datasets from Gleser and Olkin (1994)

Simulated Effect Sizes Reported by Hox (2002)

Matrix Diagonals

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Eight studies from Mak et al. (2009)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Correlation Matrices from Nohe et al. (2015)

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

Correlation Matrices from Scalco et al. (2017)

Dataset from Jaramillo, Mulki & Marshall (2005)

Compare Nested Models with Likelihood Ratio Statistic

Extract Variance-Covariance Matrix of the Random Effects

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

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

Create a Block Diagonal Matrix by Repeating the Input

Create a Block Diagonal Matrix

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Correlations from Stadler et al. (2015)

Convert a Matrix into MxMatrix-class

Parametric bootstrap on the univariate R (uniR) object

Correlation Matrices from Mathieu et al. (2015)

Extract Parameter Estimates from various classes.

create.modMatrix

Create a moderator matrix used in OSMASEM

Convert a Character Matrix into MxAlgebra-class

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Create an F matrix to select observed variables

create.mxMatrix

Create a Vector into MxMatrix-class

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

Calculate Effect Sizes using lavaan Models

Fit Models on the bootstrapped correlation matrices

Create a variance component of the heterogeneity of the random effects

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

Convert lavaan models to RAM models

Test the Homogeneity of Effect Sizes

Create a V-known matrix

Check the correctness of the RAM formulation

Convert a List of Symmetric Matrices into a Stacked Matrix

Create or Generate the Model Implied Correlation or Covariance Matrices

Create an mxModel

Create a model implied correlation matrix with implicit diagonal constraints

Convert a Matrix into a Block Diagonal Matrix

A Dataset from Cheung and Chan (2005; 2009)

Display the Accumulative Sample Sizes for the Covariance Matrix

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

metaSEM-package

Meta-Analysis using Structural Equation Modeling

One-stage meta-analytic structural equation modeling

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

A Dataset from ISSP (2005)

Calculate the R2 in OSMASEM and OSMASEM3L

Calculate the SRMR in OSMASEM and OSMASEM3L

Generate (Nested) Sample/Population Correlation/Covariance Matrices

Test Positive Definiteness of a List of Square Matrices

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Print Methods for various Objects

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Plot methods for various objects

Convert metaSEM objects into semPlotModel objects for plotting

Rerun models via mxTryHard()

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

Read External Correlation/Covariance Matrices

Compute Effect Sizes for Multiple End-point Studies

Compute Effect Sizes for Multiple Treatment Studies

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

Forty-four Studies from Cheung (2013)

Convert a Vector into a Symmetric Matrix

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

Second Stage analysis of the univariate R (uniR) approach

Conduct a Correlation/Covariance Structure Analysis with WLS

First Stage analysis of the univariate R (uniR) approach

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