Since S(t,par) = exp(-cum_hz(t,par)), we can sample from the
distribution by letting t = 0 (or some other positive number if
we want to condition on T > t_min), sampling from an exponential
distribution with lambda = rate(t, par), and then rejecting
the sample if runif(1) > S(t, par). If accepted, add that
observation to the sample, otherwise reject it, let t = t + eps
where eps is some small number, and repeat. We continue this
process until we have n observations for the sample.
Arguments
- x
The object to obtain the sampler of.
- ...
Additional arguments to pass into the survival function
Value
A function that samples from the distribution. It accepts
n, the number of samples to take, t is the time at which to start
sampling, par are the parameters of the distribution, and eps is
the update for numerical integration. Finally, we pass additional
arguments ... into the hazard function.