Model stability and variable importance plots for glmnet

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`mf` |
a fitted 'full' model, the result of a call to lm or glm. |

`nlambda` |
how many penalty values to consider. Default = 100. |

`lambda` |
manually specify the penalty values (optional). |

`B` |
number of bootstrap replications |

`penalty.factor` |
Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in exclude). Note: the penalty factors are internally rescaled to sum to nvars, and the lambda sequence will reflect this change. |

`screen` |
logical, whether or not to perform an initial screen for outliers. Highly experimental, use at own risk. Default = FALSE. |

`cores` |
number of cores to be used when parallel processing the bootstrap (Not yet implemented.) |

`force.in` |
the names of variables that should be forced into all estimated models. (Not yet implemented.) |

`...` |
further arguments (currently unused) |

The result of this function is essentially just a list. The supplied plot method provides a way to visualise the results.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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