rsp_full_sa {factor.switching}

Rotation-Sign-Permutation (RSP) algorithm (Full Simulated Annealing)

Description

Rotation-Sign-Permutation (RSP) algorithm (Full Simulated Annealing).

Usage

rsp_full_sa(lambda_mcmc, maxIter = 1000, threshold = 1e-06, verbose = TRUE, 
	sa_loops, rotate = TRUE, increaseIter = FALSE, 
	temperatureSchedule = NULL, printIter = 1000)

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: 1000.

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.

sa_loops

Number of simulated annealing loops per MCMC draw.

rotate

Logical. Default: TRUE.

increaseIter

Logical.

temperatureSchedule

Single valued function describing the temperature cooling schedule for the simulated annealing loops.

printIter

Print the progress of the algorithm when processing printIter MCMCdraws, per iteration. Default: 1000.

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.

Author(s)

Panagiotis Papastamoulis

References

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

Examples

# load small mcmc sample of 100 iterations
#	with p=6 variables and q=2 factors.
data(small_posterior_2chains)
# post-process it
reorderedPosterior <- rsp_partial_sa(
	lambda_mcmc = small_posterior_2chains[[1]], sa_loops=5)
#	sa_loops should be larger in general
# summarize the post-processed MCMC sample with coda
summary(reorderedPosterior$lambda_reordered_mcmc)