R: First Stage of the Two-Stage Structural Equation Modeling...
tssem1
R Documentation
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.
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) vectors. If it is
"Diag", a diagonal matrix is used for the random effects
meaning that the random effects are independent. 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", user has to specific 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 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/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 arithmatic 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 default), the sampling variance covariance
matrices are calculated based on individual correlation/covariance
matrix. If it is either unweighted or weighted, 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. 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.
Note
If the cluster argument is used, it returns a list of
results based for 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.