Description Usage Arguments Details Value Author(s) Examples

View source: R/logistf.control.R

Sets parameters for Newton-Raphson iteration in Firth's penalized-likelihood logistic regression.

1 2 3 4 5 6 7 8 9 | ```
logistf.control(
maxit = 25,
maxhs = 5,
maxstep = 5,
lconv = 1e-05,
gconv = 1e-05,
xconv = 1e-05,
collapse = TRUE
)
``` |

`maxit` |
The maximum number of iterations |

`maxhs` |
The maximum number of step-halvings in one iteration. The increment of the beta vector within one iteration is divided by 2 if the new beta leads to a decrease in log likelihood. |

`maxstep` |
Specifies the maximum step size in the beta vector within one iteration. |

`lconv` |
Specifies the convergence criterion for the log likelihood. |

`gconv` |
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector). |

`xconv` |
Specifies the convergence criterion for the parameter estimates. |

`collapse` |
If |

`logistf.control()`

is used by `logistf`

and `logistftest`

to set control parameters to default values.
Different values can be specified, e. g., by `logistf(..., control= logistf.control(maxstep=1))`

.

`maxit` |
The maximum number of iterations |

`maxhs` |
The maximum number of step-halvings in one iteration. The increment of the beta vector within one iteration is divided by 2 if the new beta leads to a decrease in log likelihood. |

`maxstep` |
Specifies the maximum step size in the beta vector within one iteration. |

`lconv` |
Specifies the convergence criterion for the log likelihood. |

`gconv` |
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector). |

`xconv` |
Specifies the convergence criterion for the parameter estimates. |

`collapse` |
If |

Georg Heinze

1 2 3 4 |

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