rsp_exact: Rotation-Sign-Permutation (RSP) algorithm (Exact scheme)

Description

Rotation-Sign-Permutation (RSP) algorithm (exact).

Usage

rsp_exact(lambda_mcmc, maxIter, threshold, verbose, rotate, printIter)

Value

lambda_reordered_mcmc: Post-processed MCMC sample of factor loadings.
sign_vectors: The final sign-vectors.
permute_vectors: The final permutations.
lambda_hat: The resulting average of the post-processed MCMC sample of factor loadings.
objective_function: A two-column matrix containing the time-to-reach and the value of the objective function for each iteration.

Arguments

lambda_mcmc: Input matrix containing a MCMC sample of factor loadings. The column names should read as 'LambdaV1_1',..., 'LambdaV1_q', ..., 'LambdaVp_1',..., 'LambdaVp_q', where $p$ and $q$ correspond to the number of variables and factors, respectively.
maxIter: Maximum number of iterations of the RSP algorithm. Default: 100.
threshold: Positive threshold for declaring convergence. The actual convergence criterion is threshold m p q with m denoting the number of MCMC iterations. Default: 1e-6.
verbose: Logical value indicating whether to print intermediate output or not.
rotate: Logical. Default: TRUE.
printIter: Print the progress of the algorithm when processing printIter MCMCdraws, per iteration. Default: 1000.

Author

Panagiotis Papastamoulis

References

Papastamoulis, P. and Ntzoufras, I. (2020). On the identifiability of Bayesian Factor Analytic models. arXiv:2004.05105 [stat.ME].

Examples

Run this code

# load small mcmc sample of 100 iterations
#	with p=6 variables and q=2 factors.
data(small_posterior_2chains)
# post-process it
reorderedPosterior <- rsp_exact(
	lambda_mcmc = small_posterior_2chains[[1]])
# summarize the post-processed MCMC sample with coda
summary(reorderedPosterior$lambda_reordered_mcmc)

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