plot.befa {BayesFM} R Documentation

## Plot object of class 'befa'

### Description

This function makes different plots that are useful to assess the posterior results: a trace plot of the number of latent factors (also showing Metropolis-Hastings 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.

### Usage

## S3 method for class 'befa'
plot(x, ...)


### Arguments

 x Object of class 'befa'. ... The following extra arguments can be specified: what: How to summarize the posterior distribution? what = 'maxp' (default): Only factor loadings with highest posterior probability of being different from zero or discarded from the model (if dedic = 0) are summarized. what = 'all': All factor loadings with corresponding posterior probability to be allocated to a given factor (or to be discarded from the model) larger than min.prob are summarized. what = 'hppm': Highest posterior probability models with probability larger than min.prob are summarized. byfac: Sort factor loadings by factors if TRUE, otherwise by manifest variables if FALSE (default). hpd.prob: Probability used to compute the highest posterior density intervals of the posterior distribution of the model parameters (default: 0.95). min.prob: If what = 'all', only factor loadings with posterior probability of being dedicated to a given factor (or discarded from the model) larger than this value are displayed. If what = 'hppm', only highest posterior probability models with probability larger than this value are displayed. (default: 0.20)

### Details

This function makes graphs based on the summary results returned by summary.befa. It therefore accepts the same optional arguments as this function.

### Value

No return value, called for side effects (plots the posterior results returned by summary.befa).

### Author(s)

Rémi Piatek remi.piatek@gmail.com

summary.befa to summarize posterior results.

### Examples

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')



[Package BayesFM version 0.1.5 Index]