Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
Compare multiple chains
Compute a simultaneous credible region (rectangle) from a sample for a...
tools:::Rd_package_title("factor.switching")
Plot posterior means and credible regions
Orthogonal Procrustes rotations
Rotation-Sign-Permutation (RSP) algorithm (Exact scheme)
Rotation-Sign-Permutation (RSP) algorithm (Full Simulated Annealing)
Rotation-Sign-Permutation (RSP) algorithm (Partial Simulated Annealing...
Apply sign switchings and column permutations
Weighted Orthogonal Procrustes rotations
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.