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

1.2.5

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

November 29th, 2020

Functions in metaSEM (1.2.5)

Five Published Trails from Berkey et al. (1998)

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

Correlation Matrices from Boer et al. (2016)

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

Matrix Diagonals

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

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

Ten Studies of Correlation Matrices used by Becker (2009)

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

Two Datasets from Gleser and Olkin (1994)

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

Correlation Matrices from Mathieu et al. (2015)

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

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

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

A Dataset from Bornmann et al. (2007)

Simulated Effect Sizes Reported by Hox (2002)

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

Correlation Matrices from Cooke et al. (2016)

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

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Eight studies from Mak et al. (2009)

Correlation Matrices from Nohe et al. (2015)

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

Compare Nested Models with Likelihood Ratio Statistic

Extract Variance-Covariance Matrix of the Random Effects

create.modMatrix

Create a moderator matrix used in OSMASEM

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

Correlations from Stadler et al. (2015)

Convert a Character Matrix into MxAlgebra-class

Fit Models on the bootstrapped correlation matrices

Create a Block Diagonal Matrix

Parametric bootstrap on the univariate R (uniR) object

Create a Block Diagonal Matrix by Repeating the Input

Convert a Matrix into MxMatrix-class

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Correlation Matrices from Scalco et al. (2017)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

create.mxMatrix

Create a Vector into MxMatrix-class

Dataset from Jaramillo, Mulki & Marshall (2005)

Compute Effect Sizes for Multiple End-point Studies

Rerun models via mxTryHard()

Test the Homogeneity of Effect Sizes

Create or Generate the Model Implied Correlation or Covariance Matrices

Forty-four Studies from Cheung (2013)

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

Create a variance component of the heterogeneity of the random effects

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

Display the Accumulative Sample Sizes for the Covariance Matrix

Plot methods for various objects

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

Create a V-known matrix

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

Print Methods for various Objects

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

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Convert lavaan models to RAM models

Check the correctness of the RAM formulation

Compute Effect Sizes for Multiple Treatment Studies

Convert a Matrix into a Block Diagonal Matrix

Convert a List of Symmetric Matrices into a Stacked Matrix

Create an mxModel

Calculate Effect Sizes using lavaan Models

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Extract Parameter Estimates from various classes.

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

Create an F matrix to select observed variables

Test Positive Definiteness of a List of Square Matrices

Convert metaSEM objects into semPlotModel objects for plotting

metaSEM-package

Meta-Analysis using Structural Equation Modeling

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

Conduct a Correlation/Covariance Structure Analysis with WLS

Create a model implied correlation matrix with implicit diagonal constraints

Convert a Vector into a Symmetric Matrix

One-stage meta-analytic structural equation modeling

A Dataset from ISSP (2005)

A Dataset from Cheung and Chan (2005; 2009)

Calculate the R2 in OSMASEM

First Stage analysis of the univariate R (uniR) approach

Calculate the SRMR in OSMASEM

Generate Sample/Population Correlation/Covariance Matrices

Read External Correlation/Covariance Matrices

Second Stage analysis of the univariate R (uniR) approach

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

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

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes