Create a model implied correlation matrix with implicit diagonal constraints
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)
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 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>
## Not run: ## 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) ## End(Not run)
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