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

Function to fit least angle regression path of solution for the elastic net.

1 |

`x` |
N by p matrix of predictors |

`y` |
N-vector of outcome values |

`lambda2` |
Value of L2 penalty parameter |

`normalize` |
Should columns of x be standardized? |

This function estimates the least angle regression path of solution for Ll-penalized (lasso) logistic regression
and the Cox proportional hazards model, using the R functions `enpath`

and `coxpath`

.
These latter functions use the predictor-corrector strategy devised by Park and Hastie (2007).

`beta` |
Matrix whose rows of contain the estimated coefficients for each lambda value |

`larsobj` |
Result of call to lars on augmented data |

`mx` |
Column means of x |

`sdx` |
Column standard deviations of x |

`normalize` |
Value of normalize argument in call to lars.en |

`lambda` |
Values of lambda used |

`lambda2` |
Value of lambda2 used |

`act` |
Actions (predictor added) at each step |

`maxp` |
Maximum number of predictors entered |

`call` |
Call to lars.en |

Rob Tibshirani

Zou, H. and Hastie, Trevor (2005) Regularization and Variable Selection via the Elastic Net. JRSSB 301-320,

Park, M. Y. & Hastie, T. (2007). l1-regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society Series B 69(4),

predict.lars, covTest

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

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