cv.clime | R Documentation |

Perform a k-fold cross validation for selecting lambda

cv.clime(clime.obj, loss=c("likelihood", "tracel2"), fold=5)

`clime.obj` |
clime object output from |

`loss` |
loss to be used in cross validation. Currently, two losses are available: "likelihood" and "tracel2". Default "likelihood". |

`fold` |
number of folds used in cross validation. Default 5. |

Perform a k-fold cross validation for selecting the tuning parameter
`lambda`

in clime. Two losses are implemented currently:

*
\textrm{likelihood: } Tr[Σ Ω] - \log|Ω| -
p
*

*
\textrm{tracel2: } Tr[ diag(Σ Ω - I)^2].
*

An object with S3 class `"cv.clime"`

. You can use it as a
regular R list with the following fields:

`lambdaopt` |
the lambda selected by cross validation to minimize the loss over
the grid values of |

`loss` |
the name of loss used in cross validation. |

`lambda` |
sequence of |

`loss.mean` |
average k-fold loss values for each grid value |

`loss.mean` |
standard deviation of k-fold loss values for each grid value |

`lpfun` |
Linear programming solver used. |

T. Tony Cai, Weidong Liu and Xi (Rossi) Luo

Maintainer: Xi (Rossi) Luo xi.rossi.luo@gmail.com

Cai, T.T., Liu, W., and Luo, X. (2011). *
A constrained \ell_1
minimization approach for sparse precision matrix estimation.
* Journal of the American Statistical Association 106(494): 594-607.

## trivial example n <- 50 p <- 5 X <- matrix(rnorm(n*p), nrow=n) re.clime <- clime(X) re.cv <- cv.clime(re.clime) re.clime.opt <- clime(X, re.cv$lambdaopt)

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