plot.befa {BayesFM}  R Documentation 
This function makes different plots that are useful to assess the posterior results: a trace plot of the number of latent factors (also showing MetropolisHastings acceptance across MCMC replications), a histogram of the posterior probabilities of the number of factors, heatmaps for the inficator probabilities, the factor loading matrix, and the correlation matrix of the latent factors.
## S3 method for class 'befa' plot(x, ...)
x 
Object of class 'befa'. 
... 
The following extra arguments can be specified:

This function makes graphs based on the summary results returned by
summary.befa
. It therefore accepts the same optional arguments
as this function.
No return value, called for side effects (plots the posterior results
returned by summary.befa
).
RĂ©mi Piatek remi.piatek@gmail.com
summary.befa
to summarize posterior results.
set.seed(6) # generate fake data with 15 manifest variables and 3 factors Y < simul.dedic.facmod(N = 100, dedic = rep(1:3, each = 5)) # run MCMC sampler and post process output # notice: 1000 MCMC iterations for illustration purposes only, # increase this number to obtain reliable posterior results! mcmc < befa(Y, Kmax = 5, iter = 1000) mcmc < post.column.switch(mcmc) mcmc < post.sign.switch(mcmc) # plot results for highest posterior probability model plot(mcmc, what = 'hppm')