Description Usage Arguments Details Value Author(s) References Examples

View source: R/EBelasticNet.GaussianCV.R

Hyperparameter controls degree of shrinkage, and is obtained via Cross Validation (CV). This program calculates the maximum lambda that allows one non-zero basis; and performs a search down to 0.0001*lambda_max at even steps. (20 steps)

1 | ```
EBelasticNet.GaussianCV(BASIS, Target, nFolds, Epis = "no",foldId)
``` |

`BASIS` |
sample matrix; rows correspond to samples, columns correspond to features |

`Target` |
Response each individual |

`nFolds` |
number of n-fold cv |

`Epis` |
"yes" or "no" for including two-way interactions |

`foldId` |
random assign samples to different folds |

If Epis="yes", the program adds two-way interaction K*(K-1)/2 more columns to BASIS

`CrossValidation` |
col1: hyperparameter; col2: loglikelihood mean; standard ERROR of nfold mean log likelihood |

`Lmabda_optimal` |
the optimal hyperparameter as computed |

`Alpha_optimal` |
the optimal hyperparameter as computed |

Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL

Huang, A., Xu, S., and Cai, X. (2013). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. submitted.

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EBEN documentation built on May 29, 2017, 7:28 p.m.

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