constrained_posterior: Constrained Posterior Distribution

In donaldRwilliams/BGGM: Bayesian Gaussian Graphical Models

Constrained Posterior Distribution

Description

Compute the posterior distribution with off-diagonal elements of the precision matrix constrained to zero.

Usage

constrained_posterior(
  object,
  adj,
  method = "direct",
  iter = 5000,
  progress = TRUE,
  ...
)

Arguments

object	An object of class estimate or explore
adj	A p by p adjacency matrix. The zero entries denote the elements that should be constrained to zero.
method	Character string. Which method should be used ? Defaults to the "direct sampler" (i.e., method = "direct") described in \insertCite@page 122, section 2.4, @lenkoski2013direct;textualBGGM. The other option is a Metropolis-Hastings algorithm (MH). See details.
iter	Number of iterations (posterior samples; defaults to 5000).
progress	Logical. Should a progress bar be included (defaults to TRUE) ?
...	Currently ignored.

Value

An object of class contrained, including

• precision_mean The posterior mean for the precision matrix.

• pcor_mean The posterior mean for the precision matrix.

• precision_samps A 3d array of dimension p by p by iter including the sampled precision matrices.

• pcor_samps A 3d array of dimension p by p by iter including sampled partial correlations matrices.

References

\insertAllCited

Examples

# data
Y <- bfi[,1:10]

# sample posterior
fit <- estimate(Y, iter = 100)

# select graph
sel <- select(fit)

# constrained posterior
post <- constrained_posterior(object = fit,
                              adj = sel$adj,
                              iter = 100,
                              progress = FALSE)

donaldRwilliams/BGGM documentation built on April 17, 2024, 5:52 p.m.