| post.column.switch {BayesFM} | R Documentation |
Perform column switchting on posterior MCMC sample
Description
This function reorders the columns of the factor loading matrix for each MCMC draw, as well as the rows and columns of the correlation matrix of the factors, to restore the identification of the model a posteriori with respect to the column switching problem.
Usage
post.column.switch(mcmc)
Arguments
mcmc |
Object of class ' |
Details
The reordering of the columns of the factor loading matrix is based
on the top elements of the columns (i.e., the first row containing a nonzero
factor loading in each nonzero column of \alpha, starting from the top
of the matrix). At each MCMC iteration, the nonzero columns of \alpha
are reordered such that the top elements appear in increasing order.
The rows and columns of the correlation matrix R of the factors are
switched accordingly. See section 4.3 of CFSHP (p.42) for more details.
Value
Same 'befa' object as the one passed to the function, where
the indicators in the matrix dedic, as well as the rows and columns of
the correlation matrix of the factors saved in draws, have been
switched appropriately to restore the identification of the factor model with
respect to column switching.
Author(s)
Rémi Piatek remi.piatek@gmail.com
References
G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014): “Bayesian Exploratory Factor Analysis”, Journal of Econometrics, 183(1), pages 31-57, doi:10.1016/j.jeconom.2014.06.008.
See Also
post.sign.switch to restore identification a
posteriori with respect to the sign switching problem.
Examples
set.seed(6)
Y <- simul.dedic.facmod(N = 100, dedic = rep(1:3, each = 5))
mcmc <- befa(Y, Kmax = 5, iter = 1000)
mcmc <- post.column.switch(mcmc)