The function `select.parfm()`

computes the AIC and BIC values
of parametric frailty models with different baseline hazards and different frailty distributions.

1 2 3 4 5 | ```
select.parfm(formula, cluster=NULL, strata=NULL, data, inip=NULL, iniFpar=NULL,
dist=c("exponential", "weibull", "inweibull", "frechet", "gompertz",
"loglogistic", "lognormal", "logskewnormal"),
frailty=c("none", "gamma", "ingau", "possta", "lognormal"),
method="BFGS", maxit=500, Fparscale=1, correct=0)
``` |

`formula` |
A |

`cluster` |
The name of a cluster variable in data. |

`strata` |
The name of a strata variable in data. |

`data` |
A |

`inip` |
The vector of initial values. First components are for the baseline hazard parameters according to the order given in 'details'; Other components are for the regression parameters according to the order given in 'formula'. |

`iniFpar` |
The initial value of the frailty parameter. |

`dist` |
The vector of baseline hazards' names.
It can include any of |

`frailty` |
The vector of frailty distributions' names.
It can include any of: |

`method` |
The optimisation method from the function |

`maxit` |
Maximum number of iterations (see |

`Fparscale` |
the scaling value for the frailty parameter in |

`correct` |
A correction factor that does not change the marginal log-likelihood except for an additive constant given by #clusters * correct * log(10). It may be useful in order to get finite log-likelihood values in case of many events per cluster with Positive Stable frailties. Note that the value of the log-likelihood in the output is the re-adjusted value. |

An object of class `select.parfm`

.

Federico Rotolo [aut, cre], Marco Munda [aut], Andrea Callegaro [ctb]

Munda M, Rotolo F, Legrand C (2012). parfm: Parametric Frailty Models in R. Journal of Statistical Software, 51(11), 1-20. DOI 10.18637/jss.v051.i11

`parfm`

,
`ci.parfm`

,
`predict.parfm`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(parfm)
data(kidney)
kidney$sex <- kidney$sex - 1
models <- select.parfm(Surv(time,status) ~ sex + age,
dist = c("exponential",
"weibull",
"inweibull",
"loglogistic",
"lognormal",
"logskewnormal"),
frailty = c("gamma",
"ingau",
"possta",
"lognormal"),
cluster = "id", data = kidney)
models
plot(models)
``` |

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