Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/camel.tiger.select.R

Implements the regularization parameter selection for high dimensional undirected graph estimation. The optional approaches are stability approach to regularization selection (stars) and cross validation selection (cv).

1 2 3 | ```
camel.tiger.select(est, criterion = "stars", stars.subsample.ratio = NULL,
stars.thresh = 0.1,rep.num = 20, fold = 5,
loss="likelihood", verbose = TRUE)
``` |

`est` |
An object with S3 class |

`criterion` |
Model selection criterion. |

`stars.subsample.ratio` |
The subsampling ratio. The default value is |

`stars.thresh` |
The variability threshold in stars. The default value is |

`rep.num` |
The number of subsamplings. The default value is |

`fold` |
The number of folds used in cross validation. The default value is |

`loss` |
Loss to be used in cross validation. Two losses are available: |

`verbose` |
If |

Stability approach to regularization selection (stars) is a natural way to select optimal regularization parameter for all three estimation methods. It selects the optimal graph by variability of subsamplings and tends to over-select edges in Gaussian graphical models. Besides selecting the regularization parameters, stars can also provide an additional estimated graph by merging the corresponding subsampled graphs using the frequency counts. The K-fold cross validation is also provided for selecting the parameter `lambda`

, and two loss functions are adopted as follow

*
likelihood: Tr(Σ Ω) - \log|Ω|
*

*
tracel2: Tr(diag(Σ Ω - I)^2).
*

An object with S3 class "select" is returned:

`refit` |
The optimal graph selected from the graph path |

`opt.icov` |
The optimal precision matrix selected. |

`merge` |
The graph path estimated by merging the subsampling paths. Only applicable when the input |

`variability` |
The variability along the subsampling paths. Only applicable when the input |

`opt.index` |
The index of the selected regularization parameter. |

`opt.lambda` |
The selected regularization/thresholding parameter. |

`opt.sparsity` |
The sparsity level of |

and anything else inluded in the input `est`

The model selection is NOT available when the data input is the sample covaraince matrix.

Xingguo Li, Tuo Zhao and Han Liu

Maintainer: Xingguo Li <xingguo.leo@gmail.com>

1. H. Liu and L. Wang. TIGER: A tuning-insensitive approach for optimally estimating large undirected graphs. *Technical Report*, 2012.

2. T. Cai, W. Liu, and X. Luo. A constrained *\ell_1* minimization approach to sparse precision matrix estimation. *Journal of the American Statistical Association*, 2011.

`camel.tiger`

and `camel-package`

.

1 2 3 4 5 6 7 8 9 10 11 | ```
#generate data
L = camel.tiger.generator(d = 20, graph="hub")
out1 = camel.tiger(L$data)
#model selection using stars
out1.select2 = camel.tiger.select(out1, criterion = "stars", stars.thresh = 0.05)
plot(out1.select2)
#model selection using cross validation
out1.select3 = camel.tiger.select(out1, criterion = "cv")
plot(out1.select3)
``` |

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