Description Usage Arguments Details Value Note References See Also Examples
Computes an estimate of a survival curve for recurrent event data using either the Pe<c3><b1>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<c3><b1>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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