Computes an estimate of a survival curve for recurrent event data using either the Pe<f1>a-Strawderman-Hollander, Wang-Chang or MLE Frailty estimators. It also computes the asymptotic standard errors. The resulting object of class "survfitr" is plotted by ‘plot.survfitr’, before it is returned.

1 |

`formula` |
A formula object. If a formula object is supplied it must have a Survr object as the response on the left of the ~ operator and a term on the right. For a single survival curve the "~1" part of the formula is required. |

`data` |
a data frame in wich to interpret the variables named in the formula. |

`type` |
a character string specifying the type of survival curve. Possible value are "pena-strawderman-hollander", "wang-chang" or "MLEfrailty". The default is "MLEfrailty". Only the first words are required, e.g "pe","wa","ML" |

`...` |
additional arguments passed to the type of estimator. |

See the help details of psh.fit, wc.fit or mlefrailty depending on the type chosen

a survfitr object. Methods defined for survfitr objects are provided for print,plot,lines and summary.

The mantainer wishes to thank Professors Chiung-Yu Huang and Shu-Hui Chang for their help for providing us with the Fortran code which computes standard errors of Wang and Chang's estimator.

1. Pe<f1>a, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. *J. Amer. Statist. Assoc* **96**, 1299-1315.

2. Wang, M.-C. and Chang, S.-H. (1999). Nonparametric Estimation of a Recurrent Survival Function. *J. Amer. Statist. Assoc* **94**, 146-153.

`print.survfitr`

,`plot.survfitr`

,
`lines.survfitr`

, `summary.survfitr`

,
`Survr`

,`psh.fit`

,`wc.fit`

,
`mlefrailty.fit`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data(colon)
# fit a pena-strawderman-hollander and plot it
fit<-survfitr(Survr(hc,time,event)~as.factor(dukes),data=colon,type="pena")
plot(fit,ylim=c(0,1),xlim=c(0,2000))
# print the survival estimators
fit
summary(fit)
# fit a MLE Frailty and plot it (in this case do not show s.e.)
fit<-survfitr(Survr(hc,time,event)~as.factor(dukes),data=colon,type="MLE")
plot(fit)
# print the survival estimators
fit
summary(fit)
``` |

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