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

The function performs lasso with multiple random sample splits, selecting coefficients that are simultaneously non-zero in both subsets of samples.

1 | ```
lasso.simultaneous(y, x=NULL, model='linear', nSubsampling=200, alpha=.5, lambda1=NULL, track=FALSE, ...)
``` |

`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 |

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

`nSubsampling` |
The number of random permutations, both on sample spliting and on variable scaling, default to 200. |

`alpha` |
weakness parameter: control the shrinkage of regulators. The lower alpha is, the bigger the vanishing effect on small coefficients. |

`lambda1` |
minimum lambda, default to NULL |

`track` |
logical value, whether to track the progress |

`...` |
Other parameters to be passed to the penalized function |

In each run the function splits samples randomly to two equal sets, run lasso on both sets, then select those coefficients that are simultaneously non-zero across two sets. Finally the results across many runs are summarized as the frequency of selected predictors - the higher the frequency the more confidence that the corresponding predictors are significant.

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

`beta` |
Coefficient vector |

`n` |
Number of actual subsampling, should be equal or smaller than nSubsampling in case of failing. |

`mat` |
result matrix of the subsampling |

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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