# pcLasso v1.1

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## Principal Components Lasso

A method for fitting the entire regularization path
of the principal components lasso for linear and
logistic regression models. The algorithm uses cyclic coordinate descent
in a path-wise fashion. See URL below for more information on the algorithm.
See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
<arXiv:1810.04651>.

## Readme

Bug fixes:

`predict.pcLasso`

now works when`family = “binomial”`

(previously, the intercept term was being added in an incorrect manner).- Previously,
`standardize = TRUE`

scaled the`beta`

coefficients and intercept`a0`

incorrectly. This has been fixed. `pcLasso`

now generates lambda values for the objective function RSS/(2n) + penalty, instead of that for RSS/2 + penalty.

## Functions in pcLasso

Name | Description | |

cv.pcLasso | Cross-validation for pcLasso | |

pcLasso | Fit a model with principal components lasso | |

plot.cv.pcLasso | Plot the cross-validation curve produced by "cv.pcLasso" object | |

predict.cv.pcLasso | Make predictions from a "cv.pcLasso" object | |

predict.pcLasso | Make predictions from a "pcLasso" object | |

No Results! |

## Vignettes of pcLasso

Name | ||

pcLasso.Rmd | ||

No Results! |

## Last month downloads

## Details

Type | Package |

License | GPL-3 |

URL | https://arxiv.org/abs/1810.04651 |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 6.1.1 |

VignetteBuilder | knitr |

NeedsCompilation | yes |

Packaged | 2019-02-06 00:11:42 UTC; robtibshirani |

Repository | CRAN |

Date/Publication | 2019-02-06 12:30:03 UTC |

suggests | knitr , rmarkdown |

imports | svd |

Contributors | Robert Tibshirani, Jerome Friedman, Kenneth Tay |

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