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

point-wise controled lasso stability selection

1 |

`y` |
A vector of gene expression of a probe, or a list object if x is NULL. In the latter case 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 |

`x` |
Either a matrix containing CN variables or NULL |

`alpha` |
weakness parameter: control the shrinkage of regulators, if alpha = 1 then no randomisation, if NULL then a randomly generated vector is used |

`subsampling` |
fraction of samples to use in the sampling process, default to 0.5 |

`nSubsampling` |
The number of subsampling to do, default to 200 |

`model` |
which model to use, one of "cox", "logistic", "linear", or "poisson". Default to 'linear' |

`pi_th` |
The threshold of the stability probablity for selecting a regulator. It is to determine whether a coefficient is non-zero based on the frequency it is subsampled to be non-zero, default to 0.6 |

`alpha.fwer` |
Parameter to control for the FWER, choosing alpha.fwer and alpha control the E(V), V being the number of noise variables, eg. when alpha=0.9, alpha.fwer = 1 control the E(V)<=1 |

`lambda1` |
minimum lambda to use |

`steps` |
parameter to be passed on to penalized |

`track` |
track the progress, 0 none tracking, 1 minimum amount of information and 2 full information |

`standardize` |
standardize the data or not? |

`...` |

The function first selects lambda that approximately give maximum sqrt(.8*p) predictors, while p is the number of total predictors. Then it runs lasso a number of times keeping lambda fixed. These runs are randomised with scaled predictors and subsamples. At the end, the non-zero coefficients are determined by their frequencies of selections.

A list object of class 'lol', consisting of:

`beta` |
coefficients |

`beta.bin` |
binary beta vector as thresholded by pi_th |

`mat` |
the sampling matrix, each column is the result of one sampling |

`residuals` |
residuals of regression model |

Yinyin Yuan

N. Meinshausen and P. Buehlmann (2010), Stability Selection (with discussion), Journal of the Royal Statistical Society, Series B, 72, 417-473.

lasso

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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
Non-zero coefficients: 0
from a total of 339 predictors
```

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