create.vechsR | R Documentation |
It creates implicit diagonal constraints on the model implied correlation matrix by treating the error variances as functions of other parameters.
create.vechsR(A0, S0, F0 = NULL, Ax = NULL, Sx = NULL, A.lbound=NULL, A.ubound=NULL)
A0 |
A Amatrix, which will be converted into |
S0 |
A Smatrix, which will be converted into |
F0 |
A Fmatrix, which will be converted into |
Ax |
A Amatrix of a list of Amatrix with definition variables as the moderators of the Amatrix. |
Sx |
A Smatrix of a list of Smatrix with definition variables as the moderators of the Smatrix. |
A.lbound |
A matrix of lower bound of the Amatrix. If a scalar is given, the lbound matrix will be filled with this scalar. |
A.ubound |
A matrix of upper bound of the Amatrix. If a scalar is given, the ubound matrix will be filled with this scalar. |
A list of MxMatrix-class
. The model implied correlation
matrix is computed in impliedR
and vechsR
.
Since A0
are the intercepts and Ax
are the
regression coefficients. The parameters in Ax
must be a subset of those in
A0
.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
osmasem
,
create.Tau2
, create.V
## Proposed model
model1 <- 'W2 ~ w2w*W1 + s2w*S1
S2 ~ w2s*W1 + s2s*S1
W1 ~~ w1WITHs1*S1
W2 ~~ w2WITHs2*S2
W1 ~~ 1*W1
S1 ~~ 1*S1
W2 ~~ Errw2*W2
S2 ~~ Errs2*S2'
## Convert into RAM
RAM1 <- lavaan2RAM(model1, obs.variables=c("W1", "S1", "W2", "S2"))
## No moderator
M0 <- create.vechsR(A0=RAM1$A, S0=RAM1$S, F0=NULL, Ax=NULL, Sx=NULL)
## Lag (definition variable) as a moderator on the paths in the Amatrix
Ax <- matrix(c(0,0,0,0,
0,0,0,0,
"0*data.Lag","0*data.Lag",0,0,
"0*data.Lag","0*data.Lag",0,0),
nrow=4, ncol=4, byrow=TRUE)
M1 <- create.vechsR(A0=RAM1$A, S0=RAM1$S, F0=NULL, Ax=Ax, Sx=NULL)
## Lag (definition variable) as a moderator on the correlation in the Smatrix
Sx <- matrix(c(0,"0*data.Lag",0,0,
"0*data.Lag",0,0,0,
0,0,0,"0*data.Lag",
0,0,"0*data.Lag",0),
nrow=4, ncol=4, byrow=TRUE)
M2 <- create.vechsR(A0=RAM1$A, S0=RAM1$S, F0=NULL, Ax=NULL, Sx=Sx)
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