tssem1: First Stage of the Two-Stage Structural Equation Modeling (TSSEM)

Description

It conducts the first stage analysis of TSSEM by pooling correlation/covariance matrices. tssem1FEM() and tssem1REM() use fixed- and random-effects models, respectively. tssem1() is a wrapper of these functions.

Usage

tssem1(Cov, n, method=c("REM","FEM"), cor.analysis = TRUE, cluster=NULL,
       RE.type=c("Diag", "Symm", "Zero", "User"), RE.startvalues=0.1,
       RE.lbound=1e-10, RE.constraints=NULL, I2="I2q",
       acov=c("weighted", "individual", "unweighted"), asyCovOld=FALSE,
       model.name=NULL, suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
tssem1FEM(Cov, n, cor.analysis=TRUE, model.name=NULL,
          cluster=NULL, suppressWarnings=TRUE, silent=TRUE, run=TRUE, ...)
tssem1REM(Cov, n, cor.analysis=TRUE, RE.type=c("Diag", "Symm", "Zero","User"),
          RE.startvalues=0.1, RE.lbound=1e-10, RE.constraints=NULL,
          I2="I2q", acov=c("weighted", "individual", "unweighted"),
          asyCovOld=FALSE, model.name=NULL, suppressWarnings=TRUE,
          silent=TRUE, run=TRUE, ...)

Value

Either an object of class tssem1FEM for fixed-effects TSSEM, an object of class tssem1FEM.cluster for fixed-effects TSSEM with cluster argument, or an object of class tssem1REM

for random-effects TSSEM.

Arguments

Cov: A list of correlation/covariance matrices
n: A vector of sample sizes
method: Either "REM" (default if missing) or "FEM". If it is "REM",a random-effects meta-analysis will be applied. If it is "FEM", a fixed-effects meta-analysis will be applied.
cor.analysis: Logical. The output is either a pooled correlation or a covariance matrix.
cluster: A character vector in tssem3L1 and tssemRobust1 or a vector of characters or numbers indicating the clusters in tssem1. Analyses will be conducted for each cluster. It will be ignored when method="REM".
RE.type: Either "Diag", "Symm", "Zero" or "User". If it is "Diag" (default if missing), a diagonal matrix is used for the random effects meaning that the random effects are independent. If it is "Symm", a symmetric matrix is used for the random effects on the covariances among the correlation (or covariance) vectors. If it is "Zero", there is no random effects which is similar to the conventional Generalized Least Squares (GLS) approach to fixed-effects analysis. "User", the user has to specify the variance component via the RE.constraints argument. This argument will be ignored when method="FEM".
RE.startvalues: Starting values on the diagonals of the variance component of the random effects. It will be ignored when method="FEM".
RE.lbound: Lower bounds on the diagonals of the variance component of the random effects. It will be ignored when method="FEM".
RE.constraints: A \(p*\) x \(p*\) matrix specifying the variance components of the random effects, where \(p*\) is the number of effect sizes. If the input is not a matrix, it is converted into a matrix by as.matrix(). The default is that all covariance/variance components are free. The format of this matrix follows as.mxMatrix. Elements of the variance components can be constrained equally by using the same labels. If a zero matrix is specified, it becomes a fixed-effects meta-analysis.
I2: Possible options are "I2q", "I2hm" and "I2am". They represent the I2 calculated by using a typical within-study sampling variance from the Q statistic, the harmonic mean and the arithmetic mean of the within-study sampling variances (Xiong, Miller, & Morris, 2010). More than one options are possible. If intervals.type="LB", 95% confidence intervals on the heterogeneity indices will be constructed.
acov: If it is individual, the sampling variance-covariance matrices are calculated based on individual correlation/covariance matrix. If it is either unweighted or weighted (the default), the average correlation/covariance matrix is calculated based on the unweighted or weighted mean with the sample sizes. The average correlation/covariance matrix is used to calculate the sampling variance-covariance matrices. This argument is ignored with the method="FEM" argument.
asyCovOld: Whether the old asyCov is used. See asyCov.
model.name: A string for the model name in mxModel.
suppressWarnings: Logical. If TRUE, warnings are suppressed. It is passed to mxRun.
silent: Logical. An argument to be passed to mxRun
run: Logical. If FALSE, only return the mx model without running the analysis.
...: Further arguments to be passed to mxRun

Author

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

References

Cheung, M. W.-L. (2014). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46, 29-40.

Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.

Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.