Model-implied indicator or construct variance-covariance matrix
Calculate the model-implied indicator or construct variance-covariance (VCV) matrix. Currently only the model-implied VCV for recursive linear models is implemented (including models containing second order constructs).
fit( .object = NULL, .saturated = args_default()$.saturated, .type_vcv = args_default()$.type_vcv )
.object |
An R object of class cSEMResults resulting from a call to |
.saturated |
Logical. Should a saturated structural model be used?
Defaults to |
.type_vcv |
Character string. Which model-implied correlation matrix should be calculated? One of "indicator" or "construct". Defaults to "indicator". |
Notation is taken from Bollen (1989).
If .saturated = TRUE the model-implied variance-covariance matrix is calculated
for a saturated structural model (i.e., the VCV of the constructs is replaced
by their correlation matrix). Hence: V(eta) = WSW' (possibly disattenuated).
Either a (K x K) matrix or a (J x J) matrix depending on the type_vcv.
Bollen KA (1989). Structural Equations with Latent Variables. Wiley-Interscience. ISBN 978-0471011712.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.