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
)

Arguments

.object

An R object of class cSEMResults resulting from a call to csem().

.saturated

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

.type_vcv

Character string. Which model-implied correlation matrix should be calculated? One of "indicator" or "construct". Defaults to "indicator".

Value

Either a (K x K) matrix or a (J x J) matrix depending on the type_vcv.

Details

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).

References

Bollen KA (1989). Structural Equations with Latent Variables. Wiley-Interscience. ISBN 978-0471011712.

csem(), foreman(), cSEMResults