small_posterior_2chains

<p>A list consisting of two small MCMC chains.</p>

datasets

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.

Panagiotis Papastamoulis

factor.switching

Post-Processing MCMC Outputs of Bayesian Factor Analytic Models

small_posterior_2chains function

<p>List of length 2. Each entry contains a matrix of 20 MCMC draws.</p>

small_posterior_2chains: Example data

Description

Usage

Arguments

Format