Estimated Survival Function
Estimates the marginal survival function G(T=t) of the censoring process. Compatible with single event and competing risks data.
estSurvCens(dataSet, timeColumn, eventColumns)
dataSet |
Data in original short format (data.frame). |
timeColumn |
Name of column with discrete time intervals (character scalar). |
eventColumns |
Names of the event columns of |
Named vector of estimated survival function of the censoring process for all time points except the last theoretical interval.
In the censoring survival function the last time interval [a_q, Inf) has the probability of zero.
Thomas Welchowski welchow@imbie.meb.uni-bonn.de
Gerhard Tutz and Matthias Schmid, (2016), Modeling discrete time-to-event data, Springer series in statistics, Doi: 10.1007/978-3-319-28158-2
# Load unemployment data
library(Ecdat)
data(UnempDur)
# Select subsample
subUnempDur <- UnempDur [1:100, ]
######################
# Single event example
# Estimate censoring survival function G(t)
estG <- estSurvCens(dataSet=subUnempDur, timeColumn="spell",
eventColumns="censor1")
estG
#########################
# Competing risks example
# Estimate censoring survival function G(t)
estG <- estSurvCens(dataSet=subUnempDur, timeColumn="spell",
eventColumns=c("censor1", "censor2", "censor3", "censor4"))
estGPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.