sbic: Structural Bayesian information criterion for multivariate...
In hoenlab/SBIC: Structural Bayesian Information Criterion for Graphical Models

Description Usage Arguments Value Author(s) Examples

View source: R/sbic.R

This function estimates the novel structural Bayesian information criterion given the data and a given graph structure

1	sbic(data, theta, prob, P)

`data`	A `n` by `p` dataframe representing observations
`theta`	The `p` by `p` matrix representing the given graph structure
`prob`	The expected error rate
`P`	The prior adjacency matrix

The value of sbic with given temperature parameter and prior adjacency matrix

Jie Zhou

  set.seed(1)
  d=simulate(n=100, p=100, m1 = 100, m2 = 30)
  data=d$data
  P=d$priornetwork
  theta=d$realnetwork
  prob=0.15
  index=sbic(data=data, theta=theta, prob=prob, P=P)