# This file from www.richardsconsulting.co.uk/lectures

# First load in the survival library
library(survival)

# Now create a list of (i) survival times and (ii) censoring/event status.
#
# Note that '1' = death and '0' = censored in the status variable.
example5<-list(v = c(1.0, 0.50, 0.50, 0.25, 0.25, 0.25),
               d = c(  0,    1,    0,    1,    1,    0))

# First, the direct MLE approach:
sum(example5$d) / sum(example5$v)

# Now try the exponential survival model:
expfit<-survreg(Surv(v, d) ~ 1, dist="exponential", example5)
summary(expfit)

# Note that the exponential distribution is parameterised here with lambda,
# where mu = 1 / lambda.  Also, the parameter estimates are on a log scale,
# so we need the following to recover mu as we want it:
1/exp(expfit$coef[1])

# We can calculate an approximate confidence interval as follows:
# Approximate 95% lower bound:
1/exp(expfit$coef[1] + 2*sqrt(expfit$var[1]))

# Approximate 95% upper bound:
1/exp(expfit$coef[1] - 2*sqrt(expfit$var[1]))