# Quality of estimated partial correlations

### Description

This function computed various performance measures of the estimated matrix of partial correlations.

### Usage

1 2 | ```
performance.pcor(inferred.pcor, true.pcor=NULL,
fdr=TRUE, cutoff.ggm=0.8,verbose=FALSE,plot.it=FALSE)
``` |

### Arguments

`inferred.pcor` |
matrix of estimated partial correlations |

`true.pcor` |
true matrix of partial correlations. Default is true.pcor=NULL |

`fdr` |
logical. If fdr=TRUE, the entries of |

`cutoff.ggm` |
default cutoff for significant partial correlations. Default is cutoff.ggm=0.8 |

`verbose` |
Print information on test results etc.. Default is |

`plot.it` |
Plot test results and ROC-curves. Default is |

### Details

This function computes a range of performance measures: The function always returns the number of selected edges, the binary matrix that encodes
the edges, the connectivity and the percentage of positive correlations. If `true.pcor`

is provided, the function also returns
the power (= true positive rate), the false positive rate and the positive predictive value. For non-sparse estimates that involve
testing (i.e. `fdr=TRUE`

) the function also returns the area under the curve, and a pair of vectors of false and true positive rates.
The latter can e.g. be used to plot a ROC-curve.

### Value

`num.selected` |
number of selected edges |

`adj` |
binary matrix that encodes the existence of an edge between two nodes. |

`connectivity` |
vector of length |

`positive.cor` |
percentage of positive partial correlations out of all selected edges. |

`power` |
power (if true.pcor is provided) |

`ppv` |
positive predictive value (if true.pcor is provided) |

`tpr` |
true positive rate (=power) (if true.pcor is provided) |

`fpr` |
true positive rate (=power) (if true.pcor is provided) |

`auc` |
area under the curve (if true.pcor is provided and |

`TPR` |
vector of true positive rates corresponding to varying cut-offs (if true.pcor is provided and |

`FPR` |
vector of false positive rates corresponding to varying cut-offs (if true.pcor is provided and |

### Author(s)

Juliane Schaefer, Nicole Kraemer

### References

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384