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

Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.

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`data` |
For |

`index` |
A p-vector indicating group membership of each covariate |

`type` |
model type: one of ("linear","logit", "cox") |

`maxit` |
Maximum number of iterations to convergence |

`thresh` |
Convergence threshold for change in beta |

`min.frac` |
The minimum value of the penalty parameter, as a fraction of the maximum value |

`nlam` |
Number of lambda to use in the regularization path |

`gamma` |
Fitting parameter used for tuning backtracking (between 0 and 1) |

`standardize` |
Logical flag for variable standardization prior to fitting the model. |

`verbose` |
Logical flag for whether or not step number will be output |

`step` |
Fitting parameter used for inital backtracking step size (between 0 and 1) |

`reset` |
Fitting parameter used for taking advantage of local strong convexity in nesterov momentum (number of iterations before momentum term is reset) |

`alpha` |
The mixing parameter. |

`lambdas` |
A user specified sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values |

The sequence of models along the regularization path is fit by accelerated generalized gradient descent.

An object with S3 class `"SGL"`

`beta` |
A p by |

`lambdas` |
The actual sequence of |

`type` |
Response type (linear/logic/cox) |

`intercept` |
For some model types, an intercept is fit |

`X.transform` |
A list used in |

`lambdas` |
A user specified sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values |

Noah Simon, Jerry Friedman, Trevor Hastie, and Rob Tibshirani

Maintainer: Noah Simon nrsimon@uw.edu

Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2011)
*A Sparse-Group Lasso*,

http://faculty.washington.edu/nrsimon/SGLpaper.pdf

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