This function plots a hglasso or hcov — the estimated matrix V and Z from `hglasso`

, `hcov`

, or `hbn`

1 2 |

`x` |
an object of class hglasso, hcov, or hbn. |

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

The estimated inverse covariance matrix from `hglasso`

, covariance matrix from `hcov`

, and estimated binary network `hbn`

can be decomposed as Z + V + t(V), where V is a matrix that contains hub nodes. This function creates image plots of Z and V.

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.

`plot.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 23 | ```
##############################################
# 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),0.3,0.2,2)
# image plots for the matrix V and Z
image(res1)
dev.off()
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.