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

901

Version

1.2.4

License

GPL (>= 2)

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Repository

https://github.com/mikewlcheung/metasem

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Last Published

June 14th, 2020

Functions in metaSEM (1.2.4)

Correlation Matrices from Boer et al. (2016)

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

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

Ten Studies of Correlation Matrices used by Becker (2009)

A Dataset from Bornmann et al. (2007)

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)

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

Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis

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

Selected effect sizes from Cooper et al. (2003)

Correlation coefficients reported by Tenenbaum and Leaper (2002)

Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Extract Variance-Covariance Matrix of the Random Effects

Dataset from Jaramillo, Mulki & Marshall (2005)

Simulated Effect Sizes Reported by Hox (2002)

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

Eight studies from Mak et al. (2009)

Correlation Matrices from Cooke et al. (2016)

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

Two Datasets from Gleser and Olkin (1994)

Multivariate effect sizes reported by Kalaian and Raudenbush (1996)

Check the correctness of the RAM formulation

Test Positive Definiteness of a List of Square Matrices

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

Fit Models on the bootstrapped correlation matrices

A Dataset from ISSP (2005)

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

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)

Convert a Matrix into MxMatrix-class

Matrix Diagonals

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

Correlation Matrices from Nohe et al. (2015)

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

Compare Nested Models with Likelihood Ratio Statistic

Convert a List of Symmetric Matrices into a Stacked Matrix

Convert a Matrix into a Block Diagonal Matrix

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

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

Extract Parameter Estimates from various classes.

Create an F matrix to select observed variables

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

Forty-four Studies from Cheung (2013)

Convert a Character Matrix into MxAlgebra-class

Conduct a Correlation/Covariance Structure Analysis with WLS

Create a Block Diagonal Matrix

create.mxMatrix

Create a Vector into MxMatrix-class

Create a model implied correlation matrix with implicit diagonal constraints

Create or Generate the Model Implied Correlation or Covariance Matrices

Plot methods for various objects

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

A Dataset from Cheung and Chan (2005; 2009)

Compute Effect Sizes for Multiple Treatment Studies

Three-Level Univariate Meta-Analysis with Maximum Likelihood Estimation

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

Compute Effect Sizes for Multiple End-point Studies

Read External Correlation/Covariance Matrices

Create an mxModel

Estimate Variance Components with Restricted (Residual) Maximum Likelihood Estimation

metaSEM-package

Meta-Analysis using Structural Equation Modeling

Test the Homogeneity of Effect Sizes

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

Convert lavaan models to RAM models

Correlations from Stadler et al. (2015)

Create a variance component of the heterogeneity of the random effects

Correlation Matrices from Scalco et al. (2017)

Create a Block Diagonal Matrix by Repeating the Input

Display the Accumulative Sample Sizes for the Covariance Matrix

Calculate the SRMR in OSMASEM

Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Parametric bootstrap on the univariate R (uniR) object

Create a V-known matrix

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

One-stage meta-analytic structural equation modeling

Convert metaSEM objects into semPlotModel objects for plotting

Print Methods for various Objects

Rerun models via mxTryHard()

Second Stage analysis of the univariate R (uniR) approach

Generate Sample/Population Correlation/Covariance Matrices

Calculate the R2 in OSMASEM

First Stage analysis of the univariate R (uniR) approach

Convert a Vector into a Symmetric Matrix

Extract Covariance Matrix Parameter Estimates from Objects of Various Classes

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

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