Description Usage Arguments Details Value Note See Also Examples

The main function for high-dimensional undirected graph estimation. Three graph estimation methods, including (1) Meinshausen-Buhlmann graph estimation (`mb`

) (2) graphical lasso (`glasso`

) (3) correlation thresholding graph estimation (`ct`

) and (4) tuning-insensitive graph estimation (`tiger`

), are available for data analysis.

1 2 3 4 5 6 7 8 9 10 11 12 |

`x` |
There are 2 options: (1) |

`lambda` |
A sequence of decreasing positive numbers to control the regularization when |

`nlambda` |
The number of regularization/thresholding parameters. The default value is |

`lambda.min.ratio` |
If |

`method` |
Graph estimation methods with 4 options: |

`scr` |
If |

`scr.num` |
The neighborhood size after the lossy screening rule (the number of remaining neighbors per node). ONLY applicable when |

`cov.output` |
If |

`sym` |
Symmetrize the output graphs. If |

`verbose` |
If |

The graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated via the lossy screening rule by preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a highly computationally efficient approaches correlation thresholding graph estimation.

An object with S3 class `"huge"`

is returned:

`data` |
The |

`cov.input` |
An indicator of the sample covariance. |

`ind.mat` |
The |

`lambda` |
The sequence of regularization parameters used in mb or thresholding parameters in ct. |

`sym` |
The |

`scr` |
The |

`path` |
A list of |

`sparsity` |
The sparsity levels of the graph path. |

`icov` |
A list of |

`cov` |
A list of |

`method` |
The method used in the graph estimation stage. |

`df` |
If |

`loglik` |
A |

This function ONLY estimates the graph path. For more information about the optimal graph selection, please refer to `huge.select`

.

`huge.generator`

, `huge.select`

, `huge.plot`

, `huge.roc`

, and `huge-package`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
#generate data
L = huge.generator(n = 50, d = 12, graph = "hub", g = 4)
#graph path estimation using mb
out1 = huge(L$data)
out1
plot(out1) #Not aligned
plot(out1, align = TRUE) #Aligned
huge.plot(out1$path[[3]])
#graph path estimation using the sample covariance matrix as the input.
#out1 = huge(cor(L$data), method = "glasso")
#out1
#plot(out1) #Not aligned
#plot(out1, align = TRUE) #Aligned
#huge.plot(out1$path[[3]])
#graph path estimation using ct
#out2 = huge(L$data,method = "ct")
#out2
#plot(out2)
#graph path estimation using glasso
#out3 = huge(L$data, method = "glasso")
#out3
#plot(out3)
#graph path estimation using tiger
#out4 = huge(L$data, method = "tiger")
#out4
#plot(out4)
``` |

```
Generating data from the multivariate normal distribution with the hub graph structure....done.
Conducting Meinshausen & Buhlmann graph estimation (mb)....done
Model: Meinshausen & Buhlmann graph estimation (mb)
Input: The Data Matrix
Path length: 10
Graph dimension: 12
Sparsity level: 0 -----> 0.5909091
Three plotted graphs are aligned according to the third graph
```

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