This function plots an object hglasso or hcov — graphical representation of the estimated inverse covariance matrix from `hglasso`

, covariance matrix from `hcov`

, or binary network from `hbn`

1 2 |

`x` |
an object of class |

`layout` |
the layout of the graph to use. If not specified, |

`...` |
additional parameters to be passed to |

This function plots a graphical representation of the estimated inverse covariance matrix or covariance matrix. The hubs are colored in red and has a large vertex size. Features indices for hubs are shown.

Kean Ming Tan

Tan et al. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.

`image.hglasso`

`summary.hglasso`

`hglasso`

`hcov`

`hbn`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
##############################################
# Example from Figure 1 in the manuscript
# A toy example to illustrate the results from
# Hub Graphical Lasso
##############################################
library(mvtnorm)
set.seed(1)
n=100
p=100
# A network with 4 hubs
Theta<-HubNetwork(p,0.99,4,0.1)$Theta
# Generate data matrix x
x <- rmvnorm(n,rep(0,p),solve(Theta))
x <- scale(x)
# Run Hub Graphical Lasso to estimate the inverse covariance matrix
res1 <- hglasso(cov(x),0.3,0.3,1.5)
# Graphical representation of the estimated Theta
plot(res1,main="conditional independence graph")
``` |

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