factor.switching: Post-Processing MCMC Outputs of Bayesian Factor Analytic Models

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 (2020) <arXiv:2004.05105>) 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.

Package details

AuthorPanagiotis Papastamoulis [aut, cre] (<https://orcid.org/0000-0001-9468-7613>)
MaintainerPanagiotis Papastamoulis <papapast@yahoo.gr>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the factor.switching package in your browser

Any scripts or data that you put into this service are public.

factor.switching documentation built on April 17, 2020, 1:19 a.m.