plot.befa: Plot object of class 'befa'

Description Usage Arguments Details Author(s) See Also Examples

View source: R/plot.befa.R

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

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## 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.

Author(s)

Rémi Piatek remi.piatek@gmail.com

See Also

summary.befa to summarize posterior results.

Examples

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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')

BayesFM documentation built on Oct. 23, 2020, 5:14 p.m.

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