metaSEM: tssem1 – R documentation

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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"),
       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"),
          model.name=NULL, suppressWarnings=TRUE,
          silent=TRUE, run=TRUE, ...)

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 vector of characters or numbers indicating the clusters. 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"`, a diagonal matrix is used for the random effects meaning that the random effects are independent. If it is `"Symm"` (default if missing), 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.
`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`

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.

Note

If the cluster argument is used, it returns a list of results on each cluster.

Author(s)

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.

Cheung, M. W.-L., & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 16, 28-53.