Model-implied indicator or construct variance-covariance matrix

Source: R/csem_fit.R

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 Bollen1989;textualcSEM. 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