osmasem: One-stage meta-analytic structural equation modeling

Description

It fits MASEM with the one-stage MASEM (OSMASEM) approach.

Usage

osmasem(model.name="osmasem", RAM=NULL, Mmatrix=NULL,
        Tmatrix=NULL, Jmatrix=NULL, Ax=NULL, Sx=NULL,
        A.lbound=NULL, A.ubound=NULL,
        RE.type=c("Diag", "Symm", "Zero"), data,
        subset.variables=NULL, subset.rows=NULL, 
        intervals.type = c("z", "LB"),
        mxModel.Args=NULL, mxRun.Args=NULL,
        suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
osmasem2(model.name="osmasem2", RAM, data, cor.analysis=TRUE,
         RE.type.Sigma=c("Diag", "Symm", "Zero"),
         RE.type.Mu=c("Symm", "Diag", "Zero"),
         RE.type.SigmaMu=c("Zero", "Full"),
         mean.analysis=FALSE, intervals.type=c("z", "LB"),
         startvalues=NULL, replace.constraints=FALSE,
         mxModel.Args=NULL, run=TRUE, ...)

Value

An object of class osmasem

Arguments

model.name: A string for the model name in mxModel.
RAM: A RAM object including a list of matrices of the model returned from lavaan2RAM. If it is given, Mmatrix and Tmatrix arguments will be ignored.
Mmatrix: A list of matrices of the model implied correlation matrix created by the create.vechsR. It is only required when RAM is null.
Tmatrix: A list of matrices of the heterogeneity variance-covariance matrix created by the create.Tau2. It is only required when RAM is null.
Jmatrix: The Jacobian matrix of the mean structure in mxMatrix. The covariance structure is Jmatrix %&% Tau2 + Vi. If it is not givin, an identity matrix will be used.
Ax: A Amatrix of a list of Amatrix with definition variables as the moderators of the Amatrix. It is used to create the Mmatrix.
Sx: A Smatrix of a list of Smatrix with definition variables as the moderators of the Smatrix. It is used to create the Mmatrix.
A.lbound: A matrix of lower bound of the Amatrix. If a scalar is given, the lbound matrix will be filled with this scalar.
A.ubound: A matrix of upper bound of the Amatrix. If a scalar is given, the ubound matrix will be filled with this scalar.
RE.type: Type of the random effects.
data: A list of data created by the Cor2DataFrame.
subset.variables: A character vector of the observed variables selected for the analysis.
subset.rows: A logical vector of the same length as the number of rows in the data to select which rows are used in the analysis.
intervals.type: Either z (default if missing) or LB. If it is z, it calculates the 95% confidence intervals (CIs) based on the estimated standard error. If it is LB, it calculates the 95% likelihood-based CIs on the parameter estimates.
mxModel.Args: A list of arguments passed to mxModel.
mxRun.Args: A list of arguments passed to mxRun.
suppressWarnings: Logical. If it is TRUE, warnings are suppressed. This argument is passed to mxRun.
silent: Logical. An argument is passed to mxRun
run: Logical. If FALSE, only return the mx model without running the analysis.
...: Not used yet.
cor.analysis: Whether to analyze correlation or covariance structure analysis.
RE.type.Sigma: Type of the random effects of the correlation or covariance vectors.
RE.type.Mu: Type of the random effects of the mean vectors.
RE.type.SigmaMu: Type of the random effects between the correlation/covariance vectors and the mean vectors.
mean.analysis: Whether to include the analysis of the mean structure.
startvalues: An optional list of starting values. It is useful when there are new parameters in RAM.
replace.constraints: It is relevant only when there are constraints in RAM. If it is FALSE, these constraints will be impose. If it is FALSE, the parameters on the left-hand side will be replaced by the algebras on the right-hand side.

Author

Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>

Details

osmasem was implemented based on Jak and Cheung (2020) for meta-analyzing correlation matrices. osmasem2 was a rewrite designed to handle correlation or covariance matrices, including the means. There are several major differences between them: 1. osmasem allows the use of RAM or (Mmatrix and Tmatrix), while osmasem2 calculates the Mmatrix and Tmatrix based on the RAM input. 2. RE.type is used to specify the type of random effects on the correlations in osmasem. On the contrary, osmasem2 has three types of random effects: correlations/covariances, means, and covariance between correlations/covariance and means. 3. osmasem reports the transformed random effects in the parameter table. Users have to use VarCorr to obtain the heterogeneity matrix of the random effects. In contrast, osmasem2 reports the heterogeneity matrix in the parameter table. 4. osmasem2 allows the imposition of linear and nonlinear constraints and the creation of parameter functions in RAM, which osmasem does not.

References

Jak, S., & Cheung, M. W.-L. (2020). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological Methods, 25 (4), 430-455. https://doi.org/10.1037/met0000245