Description Usage Arguments Details Value References See Also Examples

View source: R/huge.inference.R

Implements the inference for high dimensional graphical models, including Gaussian and Nonparanormal graphical models We consider the problems of testing the presence of a single edge and the hypothesis is that the edge is absent.

1 | ```
huge.inference(data, T, adj, alpha = 0.05, type = "Gaussian", method = "score")
``` |

`data` |
The input |

`T` |
The estimated inverse of correlation matrix of the data. |

`adj` |
The adjacency matrix corresponding to the graph. |

`alpha` |
The significance level of hypothesis.The default value is |

`type` |
The type of input data. There are 2 options: |

`method` |
When using nonparanormal graphical model. Test method with 2 options: |

For Nonparanormal graphical model we provide Score test method and Wald Test. However it is really slow for inferencing on Nonparanormal model, especially for large data.

An object is returned:

`data` |
The |

`p` |
The |

`error` |
The type I error of hypothesis at alpha significance level. |

1.Q Gu, Y Cao, Y Ning, H Liu. Local and global inference for high dimensional nonparanormal graphical models.

2.J Jankova, S Van De Geer. Confidence intervals for high-dimensional inverse covariance estimation. *Electronic Journal of Statistics*, 2015.

`huge`

, and `huge-package`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
#generate data
L = huge.generator(n = 50, d = 12, graph = "hub", g = 4)
#graph path estimation using glasso
est = huge(L$data, method = "glasso")
#inference of Gaussian graphical model at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out1 = huge.inference(L$data, T, L$theta)
#inference of Nonparanormal graphical model using score test at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out2 = huge.inference(L$data, T, L$theta, type = "Nonparanormal")
#inference of Nonparanormal graphical model using wald test at 0.05 significance level
T = tail(est$icov, 1)[[1]]
out3 = huge.inference(L$data, T, L$theta, type = "Nonparanormal", method = "wald")
#inference of Nonparanormal graphical model using wald test at 0.1 significance level
T = tail(est$icov, 1)[[1]]
out4 = huge.inference(L$data, T, L$theta, 0.1, type = "Nonparanormal", method = "wald")
``` |

```
Generating data from the multivariate normal distribution with the hub graph structure....done.
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 9%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 19%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 30%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 40%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 50%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 60%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 70%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 80%
Conducting the graphical lasso (glasso) wtih lossless screening....in progress: 90%
Conducting the graphical lasso (glasso)....done.
```

huge documentation built on July 1, 2021, 1:06 a.m.

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