R: Draw ROC Curve for a graph path

huge.roc {huge} | R Documentation

## Draw ROC Curve for a graph path

### Description

Draws ROC curve for a graph path according to the true graph structure

### Usage

    huge.roc(path, theta, verbose = TRUE)

### Arguments

`path` | A graph path.
`theta` | The true graph structure.
`verbose` | If `verbose = FALSE`, tracing information printing is disabled. The default value is `TRUE`.

### Details

To avoid the horizontal oscillation, false positive rates is automaically sorted in the ascent oder and true positive rates also follow the same order.

### Value

An object with S3 class "roc" is returned:

`F1` | The F1 scores along the graph path.
`tp` | The true positive rates along the graph path
`fp` | The false positive rates along the graph paths
`AUC` | Area under the ROC curve

### Note

For a lasso regression, the number of nonzero coefficients is at most `n-1`. If `d>>n`, even when regularization parameter is very small, the estimated graph may still be sparse. In this case, the AUC may not be a good choice to evaluate the performance.

### 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

    #generate data
    L = huge.generator(d = 200, graph = "cluster", prob = 0.3)
    out1 = huge(L$data)

    #draw ROC curve
    Z1 = huge.roc(out1$path,L$theta)

    #Maximum F1 score
    max(Z1$F1)

[Package huge version 1.2.7 Index]