relabeLoadings (version 1.0)
Relabel Loadings from MCMC Output for Confirmatory Factor
Analysis
Description
In confirmatory factor analysis (CFA), structural constraints
typically ensure that the model is identified up to all possible reflections,
i.e., column sign changes of the matrix of loadings. Such reflection invariance
is problematic for Bayesian CFA when the reflection modes are not well separated
in the posterior distribution. Imposing rotational constraints -- fixing
some loadings to be zero or positive in order to pick a factor solution that
corresponds to one reflection mode -- may not provide a satisfactory solution
for Bayesian CFA. The function 'relabel' uses the relabeling algorithm of
Erosheva and Curtis to correct for sign invariance in MCMC draws from CFA
models. The MCMC draws should come from Bayesian CFA models that are fit without
rotational constraints.