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

The function "camel.cmr" implements TIGER and Calibrated CLIME using L1 norm regularization

1 2 3 4 |

`data` |
There are 2 options for |

`lambda` |
A sequence of decresing positive numbers to control the regularization. Typical usage is to leave the input |

`nlambda` |
The number of values used in |

`lambda.min.ratio` |
The smallest value for |

`method` |
TIGER is applied if |

`sym` |
Symmetrization of output graphs. If |

`shrink` |
Shrinkage of regularization parameter based on precision of estimation. The default value is 1.5 if |

`prec` |
Stopping criterion. The default value is 1e-4. |

`mu` |
The smoothing parameter. The default value is 0.01. |

`max.ite` |
The iteration limit. The default value is 1e4. |

`standardize` |
All varaibles are standardized to have mean zero and standard deviation one if |

`correlation` |
Correlation matrix is used as the input of |

`perturb` |
The diagonal of |

`verbose` |
Tracing information is disabled if |

TIGER and Calibrated CLIME adjust the regularization with respect to each column of the sparse precision matrix. Thus it achieves both improved finite sample performance and tuning insensitiveness.

An object with S3 class `"tiger"`

is returned:

`data` |
The |

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

`lambda` |
The sequence of regularization parameters |

`nlambda` |
The number of values used in |

`icov` |
A list of |

`sym` |
The |

`method` |
The |

`path` |
A list of |

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

`ite` |
If |

`df` |
It is a |

`standardize` |
The |

`correlation` |
The |

`perturb` |
The |

`verbose` |
The |

Xingguo Li, Tuo Zhao, and Han Liu

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

1. T. Cai, W. Liu and X. Luo. A constrained L1 minimization approach to sparse precision matrix estimation. *Journal of the American Statistical Association*, 2011.

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

`camel-package`

, `camel.tiger.generator`

, `camel.tiger.select`

, `camel.plot`

, `camel.tiger.roc`

, `plot.tiger`

, `plot.select`

, `plot.roc`

, `plot.sim`

, `print.tiger`

, `print.select`

, `print.roc`

and `print.sim`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## generating data
n = 100
d = 100
D = camel.tiger.generator(n=n,d=d,graph="hub",g=10)
plot(D)
## sparse precision matrix estimation with method "clime"
out1 = camel.tiger(D$data, method = "clime")
plot(out1)
camel.plot(out1$path[[7]])
## sparse precision matrix estimation with method "slasso"
out2 = camel.tiger(D$data, method = "slasso")
plot(out2)
camel.plot(out2$path[[4]])
``` |

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