Description Usage Arguments Value Note Author(s) See Also Examples

It creates implicit diagonal constraints on the model implied correlation matrix by treating the error variances as functions of other parameters.

1 | ```
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>

`osmasem`

,
`create.Tau2`

, `create.V`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
## 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)
``` |

```
Loading required package: OpenMx
sh: 1: wc: Permission denied
To take full advantage of multiple cores, use:
mxOption(key='Number of Threads', value=parallel::detectCores()) #now
Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
"SLSQP" is set as the default optimizer in OpenMx.
mxOption(NULL, "Gradient algorithm") is set at "central".
mxOption(NULL, "Optimality tolerance") is set at "6.3e-14".
mxOption(NULL, "Gradient iterations") is set at "2".
```

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