Description Usage Arguments Details Value References See Also Examples
This function creates survival curves from right censored data using the prior nearignorance Dirichlet Process (IDP).
1 2 3 
formula 
a formula object, which must have a

data 
a data frame in which to interpret the variables named in the 
s 
sets the value of the prior strength s of the Dirichlet Process. 
weights 
the weights must be finite and nonnegative; it is strongly recommended that
they be strictly positive, since zero weights are ambiguous, compared
to use of the 
subset 
expression saying that only a subset of the rows of the data should be used in the fit. 
display 
determines whether the survival curves have to be plotted (TRUE) or not (FALSE). 
conf.type 
a variable saying how the credible interval shold be computed: 'exact': MonteCarlo smapling from the exact distribution, 'approx': Gaussian approximation, 'none': no credible interval is computed. 
nsamples 
number pf samples used to approximate the credible intervals
if 
conf.int 
confidence level of the credible interval. 
The estimates are obtained using the IDP estimator by Mangili and others (2014) based on the prior nearignorance Dirichlet Process model by Benavoli and others (2014).
an object of class "isurvfit"
.
See isurvfit.object
for
details. Methods defined for survfit objects are
print
and plot
.
Benavoli, A., Mangili, F., Zaffalon, M. and Ruggeri, F. (2014). Imprecise Dirichlet process with application to the hypothesis test on the probability that X < Y. ArXiv eprints, http://adsabs.harvard.edu/abs/2014arXiv1402.2755B.
Mangili, F., Benavoli, A., Zaffalon, M. and de Campos, C. (2014). Imprecise Dirichlet Process for the estimate and comparison of survival functions with censored data.
isurvfit.object
,
plot.isurvfit
,
Surv
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  data(aml)
fit < isurvfit(Surv(time, cens) ~ 1, data=aml, display=TRUE, nsamples=1000)
legend('topright', c("Lower expectation",
"Upper expectation","confidence intervals"), lty=c(1,1,2),lwd=c(1,2,1))
title("IDP survival curve (s=0.5) \nAcute Myelogenous Leukemia dataset")
data(Aids2)
dataset < Aids2
dataset["time"]<dataset[4]dataset[3]
dataset[5]<as.numeric(unlist(dataset[5]))
fit < isurvfit(Surv(time, status) ~ T.categ, dataset,s=1,
subset=(!is.na(match(T.categ, c('blood','haem','het')))),
nsamples=1000,conf.type='none')
legend('topright',c("Heterosexual contact","Hemophilia","Blood"),
title="Transmission category:",lty=c(1,1,1),col=c(1,2,3),pch=c(1,2,3))
title("IDP survival curve (s=1) \nAids dataset")
print(fit)
leukemia.surv < isurvfit(Surv(time, cens) ~ group, data = aml, display=FALSE)
plot(leukemia.surv)
legend(100, .9, c("Maintenance", "No Maintenance"), lty=c(1,1),lwd=c(2,1),
col=c('black','red'),pch=c(1,2))
title("IDP Curves\nfor AML Maintenance Study")

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