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

Lasso penalized linear regression with different optimizers

1 |

`y` |
A list object of one of the four classes: 'cv', 'stability', 'multiSplit', and 'simultaneous'. If x is NULL then y should a list of two components y and x, y is a vector of expression and x is a matrix containing copy number variables |

`...` |
other parameters |

The function contains various optimization methods for Lasso inference, such as cross-validation, randomised lasso, simultaneous lasso etc. It is specifically designed for multicollinear predictor variables.

Varied depending on the optimizer used. Generally it contains

`beta` |
coefficients |

`residuals` |
residuals of regression model |

`fit` |
the corresponding fit of regression |

Yinyin Yuan

Goeman, J. J. (2009), L1 penalized estimation in the cox proportional hazards model, Biometrical Journal. N. Meinshausen and P. Buehlmann (2010), Stability Selection (with discussion), Journal of the Royal Statistical Society, Series B, 72, 417-473. Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2009), P-values for high-dimensional regression. Journal of the American Statistical Association, 104, 1671-1681.

matrixLasso

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```
Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
Loading required package: Matrix
```

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