Description Usage Arguments Value References Examples

View source: R/smoothedLasso.r

Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the fused Lasso.

1 | ```
fusedLasso(X, y, E, lambda, gamma)
``` |

`X` |
The design matrix. |

`y` |
The response vector. |

`E` |
The adjacency matrix which encodes with a one in position |

`lambda` |
The first regularization parameter of the fused Lasso. |

`gamma` |
The second regularization parameter of the fused Lasso. |

A list with six functions, precisely the objective *u*, penalty *v*, and dependence structure *w*, as well as their derivatives *du*, *dv*, and *dw*.

Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005). Sparsity and Smoothness via the Fused Lasso. J Roy Stat Soc B Met, 67(1):91-108.

Arnold, T.B. and Tibshirani, R.J. (2020). genlasso: Path Algorithm for Generalized Lasso Problems. R package version 1.5.

Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.

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