switch_and_permute

Help function, not really meant to be used by the average user.

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

switch_and_permute function

<dl> <dt>lambda_mcmc</dt>
<dd>MCMC input.</dd> <dt>switch_vectors</dt>
<dd>Sign vectors.</dd> <dt>permute_vectors</dt>
<dd>Permutation vectors.</dd></dl>

Arguments

Author

Apply sign switchings and column permutations — switch_and_permute

<dl>

 <dt>lambda_mcmc</dt>
<dd>MCMC input.</dd>

 <dt>switch_vectors</dt>
<dd>Sign vectors.</dd>

 <dt>permute_vectors</dt>
<dd>Permutation vectors.</dd>

</dl>

switch_and_permute: Apply sign switchings and column permutations

Description

Usage

Value

Arguments

Author