Description Usage Arguments Value Note Author(s) References See Also
It conducts the first stage analysis of TSSEM by pooling
correlation/covariance matrices. tssem1FEM()
and
tssem1REM()
use fixed and randomeffects models,
respectively. tssem1()
is a wrapper of these functions.
1 2 3 4 5 6 7 8 9 10 11 12  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=1e10, 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=1e10, RE.constraints=NULL,
I2="I2q", acov=c("weighted", "individual", "unweighted"),
model.name=NULL, suppressWarnings=TRUE,
silent=TRUE, run=TRUE, ...)

Cov 
A list of correlation/covariance matrices 
n 
A vector of sample sizes 
method 
Either 
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 
RE.type 
Either 
RE.startvalues 
Starting values on the
diagonals of the variance component of the random effects. It will be ignored when 
RE.lbound 
Lower bounds on the diagonals of the variance
component of the random effects. It will be ignored when

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

I2 
Possible options are 
acov 
If it is 
model.name 
A string for the model name in 
suppressWarnings 
Logical. If 
silent 
Logical. Argument to be passed to 
run 
Logical. If 
... 
Further arguments to be passed to 
Either an object of class tssem1FEM
for fixedeffects TSSEM,
an object of class tssem1FEM.cluster
for fixedeffects TSSEM
with cluster
argument, or an object of class tssem1REM
for randomeffects TSSEM.
If the cluster
argument is used, it returns a list of
results on each cluster.
Mike W.L. Cheung <[email protected]>
Cheung, M. W.L. (2014). Fixed and randomeffects metaanalytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46, 2940.
Cheung, M. W.L. (2013). Multivariate metaanalysis as structural equation models. Structural Equation Modeling, 20, 429454.
Cheung, M. W.L., & Chan, W. (2005). Metaanalytic structural equation modeling: A twostage approach. Psychological Methods, 10, 4064.
Cheung, M. W.L., & Chan, W. (2009). A twostage approach to synthesizing covariance matrices in metaanalytic structural equation modeling. Structural Equation Modeling, 16, 2853.
wls
, Cheung09
,
Becker92
, Digman97
, issp89
, issp05
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