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

Fits and cross-validates a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter, and a parameter value is chosen by cross-validation. 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) |

`nfold` |
Number of folds of the cross-validation loop |

`standardize` |
Logical flag for variable standardization (scaling) 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 inputted sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values |

`foldid` |
An optional user-pecified vector indicating the cross-validation fold in which each observation should be included. Values in this vector should range from 1 to nfold. If left unspecified, SGL will randomly assign observations to folds |

The function runs `SGL`

`nfold`

+1 times; the initial run is to find the `lambda`

sequence, subsequent runs are used to compute the cross-validated error rate and its standard deviation.

An object with S3 class `"cv.SGL"`

`lldiff` |
An |

`llSD` |
An |

`lambdas` |
The actual list of |

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

`fit` |
A model fit object created by a call to |

`foldid` |
A vector indicating the cross-validation folds that each observation is assigned to |

`prevals` |
A matrix of prevalidated predictions for each observation, for each lambda-value |

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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