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

View source: R/rags2ridgesDepr.R

This function is now deprecated. Please use `optPenalty.kCVauto`

instead.

Function that performs an 'automatic' search for the optimal penalty parameter for the `ridgeP`

call by employing Brent's method to the calculation of a cross-validated negative log-likelihood score.

1 2 3 4 | ```
optPenalty.LOOCVauto(Y, lambdaMin, lambdaMax,
lambdaInit = (lambdaMin + lambdaMax)/2,
cor = FALSE, target = default.target(covML(Y)),
type = "Alt")
``` |

`Y` |
Data |

`lambdaMin` |
A |

`lambdaMax` |
A |

`lambdaInit` |
A |

`cor` |
A |

`target` |
A target |

`type` |
A |

The function determines the optimal value of the penalty parameter by application of the Brent algorithm (1971) to the (leave-one-out) cross-validated negative log-likelihood score (using a regularized ridge estimator for the precision matrix). The search for the optimal value is automatic in the sense that in order to invoke the root-finding abilities of the Brent method, only a minimum value and a maximum value for the penalty parameter need to be specified as well as a starting penalty value. The value at which the (leave-one-out) cross-validated negative log-likelihood score is minimized is deemed optimal. The function employs the Brent algorithm as implemented in the optim function.

An object of class `list`

:

`optLambda` |
A |

`optPrec` |
A |

When `cor = TRUE`

correlation matrices are used in the computation of the (cross-validated) negative
log-likelihood score, i.e., the leave-one-out sample covariance matrix is a matrix on the correlation scale.
When performing evaluation on the correlation scale the data are assumed to be standardized.
If `cor = TRUE`

and one wishes to used the default target specification one may consider using `target = default.target(covML(Y, cor = TRUE))`

. This gives a default target under the assumption of standardized data.

Wessel N. van Wieringen, Carel F.W. Peeters <[email protected]>

Brent, R.P. (1971). An Algorithm with Guaranteed Convergence for Finding a Zero of a Function. Computer Journal 14: 422-425.

`GGMblockNullPenalty`

, `GGMblockTest`

, `ridgeP`

, `optPenalty.aLOOCV`

,
`optPenalty.LOOCV`

,

`default.target`

, `covML`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
## Obtain regularized precision under optimal penalty
OPT <- optPenalty.LOOCVauto(X, lambdaMin = .001, lambdaMax = 30); OPT
OPT$optLambda # Optimal penalty
OPT$optPrec # Regularized precision under optimal penalty
## Another example with standardized data
X <- scale(X, center = TRUE, scale = TRUE)
OPT <- optPenalty.LOOCVauto(X, lambdaMin = .001, lambdaMax = 30, cor = TRUE,
target = default.target(covML(X, cor = TRUE))); OPT
OPT$optLambda # Optimal penalty
OPT$optPrec # Regularized precision under optimal penalty
``` |

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