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,
  ...
)
A single numeric value giving the distance between two matrices.
An R object of class cSEMResults resulting from a call to csem().
A matrix to compare.
A matrix to compare.
Logical. Should a saturated structural model be used?
Defaults to FALSE.
Ignored.
calculateDG(): The geodesic distance (dG).
calculateDL(): The squared Euclidean distance
calculateDML(): The distance measure (fit function) used by ML
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.