R: Graph visualization

huge.plot {huge} | R Documentation

## Graph visualization

### Description

Implements the graph visualization using adjacency matrix. It can automatic organize 2D embedding layout.

### Usage

    huge.plot(G, epsflag = FALSE, graph.name = "default", cur.num = 1,
    location)

### Arguments

`G` | The adjaceny matrix corresponding to the graph.
`epsflag` | If `epsflag = TRUE`, save the plot as an eps file in the target directory. The default value is `FALSE`.
`graph.name` | The name of the output eps files. The default value is "default".
`cur.num` | The number of plots saved as eps files. Only applicale when `epsflag = TRUE`. The default value is 1.
`location` | Target directory. The default value is the current working directory.

### Details

The user can change `cur.num` to plot several figures and select the best one. The implementation is based on the popular package "igraph".

### Author(s)

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, and Larry Wasserman
Maintainers: Tuo Zhao<tzhao5@jhu.edu>

### References

1. T. Zhao and H. Liu. The huge Package for High-dimensional Undirected Graph Estimation in R. Journal of Machine Learning Research, 2012
2. H. Liu, F. Han, M. Yuan, J. Lafferty and L. Wasserman. High Dimensional Semiparametric Gaussian Copula Graphical Models. Annals of Statistics,2012
3. D. Witten and J. Friedman. New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics, to appear, 2011. 4. Han Liu, Kathryn Roeder and Larry Wasserman. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Advances in Neural Information Processing Systems, 2010.
5. R. Foygel and M. Drton. Extended bayesian information criteria for gaussian graphical models. Advances in Neural Information Processing Systems, 2010.
6. H. Liu, J. Lafferty and L. Wasserman. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research, 2009
7. J. Fan and J. Lv. Sure independence screening for ultra-high dimensional feature space (with discussion). Journal of Royal Statistical Society B, 2008.
8. O. Banerjee, L. E. Ghaoui, A. d'Aspremont: Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. Journal of Machine Learning Research, 2008.
9. J. Friedman, T. Hastie and R. Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 2008.
10. J. Friedman, T. Hastie and R. Tibshirani. Sparse inverse covariance estimation with the lasso, Biostatistics, 2007.
11. N. Meinshausen and P. Buhlmann. High-dimensional Graphs and Variable Selection with the Lasso. The Annals of Statistics, 2006.

### See Also

`huge` and `huge-package`

### Examples

    ## visualize the hub graph
    L = huge.generator(graph = "hub")
    huge.plot(L$theta)

    ## visualize the band graph
    L = huge.generator(graph = "band",g=5)
    huge.plot(L$theta)

    ## visualize the cluster graph
    L = huge.generator(graph = "cluster")
    huge.plot(L$theta)

    #show working directory
    getwd()
    #plot 5 graphs and save the plots as eps files in the working directory
    huge.plot(L$theta, epsflag = TRUE, cur.num = 5)

[Package huge version 1.2.7 Index]