# Estimation of the ridge parameter

### Description

Maximum likelihood estimation of the ridge parameter by cross-validation

### Usage

1 2 3 |

### Arguments

`dat` |
the data matrix. |

`X` |
the design matrix. |

`weights` |
weights on the cases of the design matrix. |

`refs` |
a vector specifying validation group membership. Default is to
construct |

`tol` |
the sensitivity in calculations near zero. |

`only.ridge` |
logical, whether only the ridge Parameters should be passed back or additionally the Cross Validation penalised likelihood. |

`doPlot` |
logical, whether a plot of -2logL vs a candidate for the ridge parameter should be drawn. |

`col` |
color of Plot symbols. |

`type` |
type of Plot symbols. |

`...` |
further plot arguments. |

### Details

This function estimates the ridge parameter when applying ridge regularization to a sample correlation matrix of residuals. The ridge parameter is estimated to maximize the normal likelihood as estimated via cross validation (Warton 2008).

### Value

A list with the following component:

`ridgeParameter` |
the estimated ridge parameter |

If `only.ridge=FALSE`

the returned list additionally contains the element:

`minLL` |
the minimum of the negative log-likelihood |

.

### Author(s)

David Warton <David.Warton@unsw.edu.au> and Ulrike Naumann.

### References

Warton D.I. (2008). Penalized normal likelihood and ridge regularization of
correlation and covariance matrices. *Journal of the American
Statistical Association* 103, 340-349.

### See Also

`manylm`

### Examples

1 2 3 4 5 | ```
data(spider)
spiddat <- mvabund(spider$abund)
X <- spider$x
ridgeParamEst(dat = spiddat, X = model.matrix(spiddat~X))
``` |

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