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

Function to fit least angle regression path of solution for Ll-penalized (lasso) logistic regression and the Cox proportional hazards model.

1 |

`x` |
N by p matrix of predictors |

`y` |
N-vector of outcome values |

`status` |
Optional N-vector of censoring indicators for Cox Proportioanl hazards model. 1=failed; 0=censored. |

`family` |
"binomial" or "cox" |

`standardize` |
Should predictor be standardized? Default TRUE |

`frac.arclength` |
Step length parameter for |

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 `glmpath`

and `coxpath`

written by Park and Hastie.
These latter functions use the predictor-corrector strategy devised by Park and Hastie (2007).
An additional L2 penalty can be added for stability.

`beta ` |
Matrix of estimated coefficients, with LAR steps in the rows. |

`a0` |
Estimate of intercept |

`lambda0` |
Raw values of lambda used |

`lambda` |
Values of lambda multiplied by sdx, the standard deviation of each predictor |

`lambda2` |
Value of lambda2 (L2 penalty parameter) |

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

`maxp` |
Maximum number of predictors entered |

`family` |
family used- "binomial" or "cox" |

`call` |
Call to lars.glm |

`pathobj` |
Result of call to glmpath or coxpath |

Rob Tibshirani

Park, M.Y. and Hastie, T. (2007) 1l regularization path algorithm for generalized linear models. JRSSB B 69(4), 659-677

covTest, predict.glm.Rd

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