constrained_posterior: Constrained Posterior Distribution In BGGM: Bayesian Gaussian Graphical Models

Description

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

Usage

 ```1 2 3 4 5 6 7 8``` ```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.

\insertAllCited

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# 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) ```

BGGM documentation built on Aug. 20, 2021, 5:08 p.m.