Description Usage Arguments Value Author(s) References Examples

The imputation regularized optimization (IRO) algorithm for learning high-dimensional Gaussian Graphical Models with simulated incomplete data.

1 |

` data ` |
p Dataset with missing values. |

` A ` |
True adjacency matrix for evaluating the performance of the IRO algorithm. |

` alpha1 ` |
The significance level of correlation screening in the |

` alpha2 ` |
The significance level of |

` alpha3 ` |
The significance level of integrative |

` iteration ` |
The number of total iterations, the default value is 30. |

` warm ` |
The number of burn-in iterations, the default value is 10. |

` RecPre ` |
The output of Recall and Precision values for the IRO algorithm. |

` Adj ` |
p Estimated adjacency matrix by our IRO algorithm. |

Bochao Jiajbc409@ufl.edu and Faming Liang

Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.

Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.

1 2 3 4 5 6 7 8 9 | ```
library(IROmiss)
library(huge)
result <- SimGraDat(n = 200, p = 100, type = "band", rate = 0.1)
Est <- GraphIRO(result$data, result$A, iteration = 20, warm = 10)
## plot network by our estimated adjacency matrix.
huge.plot(Est$Adj)
## plot the Recall-Precision curve.
plot(Est$RecPre[,1], Est$RecPre[,2], type="l", xlab="Recall", ylab="Precision")
``` |

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