Description Usage Arguments Details Value Note See Also Examples

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

1 |

`path` |
A graph path. |

`theta` |
The true graph structure. |

`verbose` |
If |

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

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 |

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.

`huge`

and `huge-package`

.

1 2 3 4 5 6 7 8 9 | ```
#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)
``` |

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