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

### Description

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

### Usage

1 |

### Arguments

`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? |

### Details

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).

### Value

`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 |

### Author(s)

Rob Tibshirani

### References

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),

### See Also

predict.lars, covTest

### Examples

1 2 3 4 5 6 7 |

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