plot.stabpath {c060} | R Documentation |

## function to plot a stability path

### Description

Given a desired family-wise error rate (FWER) and a stability path calculated with `stability.path`

the function selects an stable set of features and plots the stability path and the corresponding regularization path.

### Usage

```
## S3 method for class 'stabpath'
plot(x, error=0.05, type=c("pfer","pcer"), pi_thr=0.6, xvar=c("lambda", "norm", "dev"),
col.all="black", col.sel="red", ...)
```

### Arguments

`x` |
an object of class "stabpath" as returned by the function |

`error` |
the desired type I error level w.r.t. to the chosen type I error rate. |

`type` |
The type I error rate used for controlling the number falsely selected variables. If |

`pi_thr` |
the threshold used for the stability selection, should be in the range of 0.5 > pi_thr < 1. |

`xvar` |
the variable used for the xaxis, e.g. for "lambda" the selection probabilities are plotted along the log of the penalization parameters, for "norm" along the L1-norm and for "dev" along the fraction of explained deviance. |

`col.all` |
the color used for the variables that are not in the estimated stable set |

`col.sel` |
the color used for the variables in the estimated stable set |

`...` |
further arguments that are passed to matplot |

### Value

a list of four objects

`stable` |
a vector giving the positions of the estimated stable variables |

`lambda` |
the penalization parameter used for the stability selection |

`lpos` |
the position of the penalization parameter in the regularization path |

`error` |
the desired type I error level w.r.t. to the chosen type I error rate |

`type` |
the type I error rate |

### Author(s)

Martin Sill \ m.sill@dkfz.de

### References

Meinshausen N. and Buehlmann P. (2010), Stability Selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology) Volume 72, Issue 4, pages 417-473.

Sill M., Hielscher T., Becker N. and Zucknick M. (2014), *c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22.*
doi:10.18637/jss.v062.i05

### See Also

### Examples

```
## Not run:
#gaussian
set.seed(1234)
x=matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000]%*%c(rep(2,5),rep(-2,5),rep(.1,990))
res <- stabpath(y,x,weakness=1,mc.cores=2)
plot(res,error=.5,type='pfer')
## End(Not run)
```

*c060*version 0.3-0 Index]