BDgraph (version 2.3)

select.g: Selecting the best graphical models based on BDMCMC algorithm

Description

According to output of the BDMCMC algorithm, this function gives us the graphical models with the highest posterior probabilities.

Usage

select.g(output, g = 1)

Arguments

output

a list which is the result of the BDMCMC algorithm from the 'bdmcmc', 'bdmcmc.low', or 'bdmcmc.high' functions.

g

you can select the number of graphical models with highest probabilities (default is 1).

References

Mohammadi, A. and E. C. Wit (2012). Gaussian graphical model determination based on birth-death MCMC inference, arXiv:1210.5371v4. http://arxiv.org/abs/1210.5371v4

See Also

bdmcmc and prob.allg

Examples

p <- 8 # number of nodes 
  # "truK" is the precision matrix of true graph
  truK <- diag(p)
  for (i in 1:(p-1)) truK[i,i+1] <- truK[i+1,i] <- 0.5
  truK[1,p] <- truK[p,1] <- 0.4
  truK # precision matrix of the true graph
  
  # generate the data (200 observations) from multivariate normal 
  # distribution with mean zero and percision matrix "truK"
  data <- mvrnorm(200, c(rep(0,p)), solve(truK))
  
  output <- bdmcmc(data, meanzero = T, iter = 2000)
  select.g(output)