distance_measures {cSEM} | R Documentation |
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 |
.matrix1 |
A |
.matrix2 |
A |
.saturated |
Logical. Should a saturated structural model be used?
Defaults to |
... |
Ignored. |
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.
A single numeric value giving the distance between two matrices.
calculateDG()
: The geodesic distance (dG).
calculateDL()
: The squared Euclidean distance
calculateDML()
: The distance measure (fit function) used by ML