omegasCFA() no longer reports a zero-width
confidence interval when the standard errors of the omega coefficients
could not be computed. lavaan returns the point estimate as both
interval bounds when the information matrix cannot be inverted, which
reads as a perfectly precise estimate. The interval is now
NA and a warning is issued. This affects in particular the
second-order model with two group factors, where the general factor is
not identified: the general factor has one loading per group factor, but
the group factors supply only a single correlation. The point estimates
are still returned.
omegasCFA() no longer fails entirely when
fit.measures = TRUE and the fit measures cannot be
computed, which lavaan refuses for a model that did not converge. The
coefficients are returned and the fit measures are omitted.
omegasCFA() now warns when the factor model did not
converge, and when the solution is inadmissible, for instance because of
a negative variance estimate. Previously a non-converged model returned
plausible-looking coefficients without any indication.
omegasCFA() gained
diagnostics, a list with the entries
converged, admissible, and
se.available, and lavaan.fit, holding the
fitted lavaan object. print() reports the diagnostics when
any of them fail.bomegas() now calls the function supplied to
callback during the Gibbs sampling loops. Previously the
argument was accepted but never invoked, so external progress reporting
never advanced.
bomegas() no longer errors for
model.type = "bi-factor" in combination with
param.out = TRUE. The array holding the posterior samples
of the factor variances was allocated without room for the general
factor.