Description Usage Arguments Details Value Author(s) Examples

L1 penalized estimation of multistate models.

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`type` |
character defining the type of penalty, either |

`d` |
data set with variables (mandatory) |

`X` |
design matrix. |

`PSM1` |
penalty structure matrix containing the penalty structure vectors |

`PSM2` |
penalty structure matrix containing the penalty structure vectors |

`lambda1` |
vector with penalty parameters for the respective penalty components (lasso part). |

`lambda2` |
vector with penalty parameters for the respective penalty components (fusion part). |

`w` |
vector containing weights for the respective penalty components. |

`betastart` |
vector containing starting values for beta. |

`nu` |
numeric value denoting the weight, i.e. a value between 0 and 1, of the Fisher scoring updates. |

`tol` |
relative update tolerance for stopping of the estimation algorithm. |

`max.iter` |
number of maximum iterations if tlerance is not reached. |

`trace` |
logical triggering printout of status information during the fitting process. . |

`diagnostics` |
logical triggering that Fisher matrix, score vector, and approximated penalty matrix are returned with the results. |

`family` |
character defining the likelihood to be used. |

`poissonresponse` |
response values for poisson likelihood (if used). |

`poissonoffset` |
offset values for poisson likelihood (if used). |

`constant.approx` |
constant for locally squared approximation of the absolute value penalty function. |

This function is the core function of this package. It implements L1 penalized estimation of multistate models, with the penalty applied to absolute effects and absolute effect differences on transition-type specific hazard rates.

A list with elements `B`

(matrix with estimated
effects), `aic`

(Akaike Information Criterion), `gcv`

(GCV
criterion), `df`

(degrees of freedom), and (if `diagnostics`

are requested)
`F`

(Fisher matrix), `s`

(score vector), and
`A`

(approximated penalty matrix).

Holger Reulen

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