Calculate the difference between the empirical (S) and the model-implied indicator variance-covariance matrix (Sigma_hat) using different distance measures.

```
calculateDG(
.object = NULL,
.matrix1 = NULL,
.matrix2 = NULL,
.saturated = FALSE,
...
)
calculateDL(
.object = NULL,
.matrix1 = NULL,
.matrix2 = NULL,
.saturated = FALSE,
...
)
calculateDML(
.object = NULL,
.matrix1 = NULL,
.matrix2 = NULL,
.saturated = FALSE,
...
)
```

- .object
An R object of class cSEMResults resulting from a call to

`csem()`

.- .matrix1
A

`matrix`

to compare.- .matrix2
A

`matrix`

to compare.- .saturated
Logical. Should a saturated structural model be used? Defaults to

`FALSE`

.- ...
Ignored.

A single numeric value giving the distance between two matrices.

The distances may also be computed for any two matrices A and B by supplying
A and B directly via the `.matrix1`

and `.matrix2`

arguments.
If A and B are supplied `.object`

is ignored.

`calculateDG`

: The geodesic distance (dG).`calculateDL`

: The squared Euclidean distance`calculateDML`

: The distance measure (fit function) used by ML