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

Arguments

my.df

A list of correlation/covariance matrices

A vector of sample sizes

method

Either "FEM" (default if missing) or "REM". If it is "FEM", fixed-effects meta-analysis will be applied. If it is "REM", random-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 "Symm", "Diag", "Zero" or "User". If it is "Symm" (default if missing), a symmetric matrix is used for the random effects on the covariances among the correlation (or covariance)

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 componets 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/varian

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 arithmatic mean of the

acov

If it is individual (the default), the sampling variance covariance matrices are calculated based on individual correlation/covariance matrix. If it is either unweighted or weighted, the average correlation/covari

model.name

A string for the model name in mxModel.

suppressWarnings

Logical. If TRUE, warnings are suppressed. Argument to be passed to mxRun.

silent

Logical. 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.

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.

Description

Usage

Arguments

Value

References

See Also