tssem1 | R Documentation |
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.
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, ...)
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 character vector in |
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 |
I2 |
Possible options are |
acov |
If it is |
asyCovOld |
Whether the old |
model.name |
A string for the model name in |
suppressWarnings |
Logical. If |
silent |
Logical. An argument to be passed to |
run |
Logical. If |
... |
Further arguments to be passed to |
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.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
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.
wls
, Cheung09
,
Becker92
, Digman97
, issp89
, issp05
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