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Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | factor.switching |
Post-Processing MCMC Outputs of Bayesian Factor Analytic Models |
Statements
12 February 2024
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A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <doi:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
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