Description Usage Arguments Details Value Note See Also

Fits a `rsm`

model without computing the model matrix of the
response vector.

1 2 |

`X` |
the model matrix (design matrix). |

`Y` |
the response vector. |

`offset` |
optional offset added to the linear predictor. |

`family` |
a |

`dispersion` |
if |

`score.dispersion` |
must default to |

`maxit` |
maximum number of iterations. |

`epsilon` |
convergence threshold. |

`trace` |
if |

`...` |
not used, but do absorb any redundant argument. |

The `rsm.surv`

function is called internally by the
`rsm`

routine to do the actual model fitting. Although
it is not intended to be used directly by the user, it may be useful
when the same data frame is used over and over again. It might save
computational time, since the model matrix is not created. No
formula needs to be specified as an argument. As no `weights`

argument is available, the response `Y`

and the model matrix
`X`

must already include the weights if weighting is desired.

an object, which is a subset of a `rsm`

object.

The `rsm.surv`

function is the default option for
`rsm`

for the `extreme`

, `logistic`

,
`logWeibull`

, `logExponential`

, `logRayleigh`

and
`student`

(with `df`

larger than 2) error distributions.
It makes use of the `survreg.fit`

routine to
estimate parametric survival models. It receives `X`

and
`Y`

data rather than a formula, but still uses the
`family.rsm`

object to define the IRLS steps. The
`rsm.surv`

routine cannot be used for Huber-type and
user-defined error distributions.

`rsm`

, `rsm.fit`

, `rsm.null`

,
`rsm.object`

, `rsm.families`

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