metaSEM package - RDocumentation

Learn R Programming

⚠️There's a newer version (1.5.0) of this package.Take me there.

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")

Copy Link

Version

Install

install.packages('metaSEM')

Monthly Downloads

1,010

Version

1.3.1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/mikewlcheung/metasem

Maintainer

Mike Cheung

Last Published

August 9th, 2023

Functions in metaSEM (1.3.1)

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

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

Two Datasets from Gleser and Olkin (1994)

Matrix Diagonals

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

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)

Simulated Effect Sizes Reported by Hox (2002)

Correlation Matrices from Cooke et al. (2016)

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Correlation Matrices from Scalco et al. (2017)

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

Correlation Matrices from Nohe et al. (2015)

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

Eight studies from Mak et al. (2009)

Dataset from Jaramillo, Mulki & Marshall (2005)

Correlation Matrices from Mathieu et al. (2015)

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

Convert a Character Matrix into MxAlgebra-class

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Correlations from Stadler et al. (2015)

Extract Variance-Covariance Matrix of the Random Effects

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

Convert a Matrix into MxMatrix-class

Compare Nested Models with Likelihood Ratio Statistic

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Extract Parameter Estimates from various classes.

Create an F matrix to select observed variables

Check the correctness of the RAM formulation

Create a Block Diagonal Matrix by Repeating the Input

Calculate Effect Sizes using lavaan Models

Create a Block Diagonal Matrix

Create a V-known matrix

Create a variance component of the heterogeneity of the random effects

create.mxMatrix

Create a Vector into MxMatrix-class

A Dataset from Cheung and Chan (2005; 2009)

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

Create an mxModel

Fit Models on the bootstrapped correlation matrices

Parametric bootstrap on the univariate R (uniR) object

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

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

create.modMatrix

Create a moderator matrix used in OSMASEM

Convert lavaan models to RAM models

Display the Accumulative Sample Sizes for the Covariance Matrix

A Dataset from ISSP (2005)

Test the Homogeneity of Effect Sizes

Test Positive Definiteness of a List of Square Matrices

Convert a Matrix into a Block Diagonal Matrix

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

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Calculate the SRMR in OSMASEM and OSMASEM3L

Calculate the R2 in OSMASEM and OSMASEM3L

Rerun models via mxTryHard()

One-stage meta-analytic structural equation modeling

Plot methods for various objects

Read External Correlation/Covariance Matrices

Print Methods for various Objects

Generate (Nested) Sample/Population Correlation/Covariance Matrices

Compute Effect Sizes for Multiple Treatment Studies

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

Convert metaSEM objects into semPlotModel objects for plotting

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

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

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

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

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

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

Second Stage analysis of the univariate R (uniR) approach

First Stage analysis of the univariate R (uniR) approach

Compute Effect Sizes for Multiple End-point Studies

Conduct a Correlation/Covariance Structure Analysis with WLS

Convert a Vector into a Symmetric Matrix

Forty-four Studies from Cheung (2013)

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

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)

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

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

Ten Studies of Correlation Matrices used by Becker (2009)

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

Correlation Matrices from Boer et al. (2016)

A Dataset from Bornmann et al. (2007)

Five Published Trails from Berkey et al. (1998)

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