It creates variance component of the heterogeneity of the random effects by decomposing the variance component into matrices of correlation and standard deviations.
create.Tau2(RAM, no.var, Tau1.labels=seq(no.var),
RE.type = c("Diag", "Symm", "Zero", "User"),
level=c("single", "between", "within"),
RE.User=NULL, Transform = c("expLog", "sqSD"),
RE.startvalues=0.05)
A list of MxMatrix-class
. The variance component is
computed in Tau2
.
The RAM model for testing. no.var
is calculated from it.
If RAM
is missing, the user has to specify
the no.var
argument. It represents the no.var
by no.var
of the random effects).
Parameter labels in Tau1
. The default is
Tau1_1
, Tau1_2
, etc.
Either "Diag"
, "Symm"
, "Zero"
or "User"
. If
it is"Diag"
(the default if missing), a diagonal matrix is used
for the random effects meaning that the random effects are
independent. If it is "Symm"
, a symmetric matrix is used for the random effects on the covariances
among the correlation (or covariance) vectors. If it is
"Zero"
, a zero matrix is assumed on the variance component of
the random effects. If it is "User"
, users have to specify the
RE.User
argument.
whether it is for single-level, between-, or within-level analyses. The only difference are the names of the matrices.
It represents the no.var
by no.var
symmetric matrix
of TRUE
or FALSE
for the variance component. If the
elements are FALSE
, they are fixed at 0.
Either "expLog"
or "sqSD"
. If it is
"expLog"
, the variances are estimated by applying a log and exp
transformation. If it is "sqSD"
, the variances are estimated by
applying a square on the SD. The transformation may improve the
estimation when the heterogeneity is small or close to zero.
Starting values for the variances.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
osmasem
,
create.V
, create.vechsR
# \donttest{
T0 <- create.Tau2(no.var=4, RE.type="Diag", Transform="expLog", RE.startvalues=0.05)
T0
T1 <- create.Tau2(no.var=4, Tau1.labels=c("a", "b", "c", "d"))
T1
# }
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