survexp.fit(x, y, times, death, ratetable)ratetable,
in the correct order.survexp.uswhite.survexp.
Consequently, this function has very few error checks on its input arguments.
For an exact estimate times should be a superset of y, so that each
subject at risk is at risk for the entire sub-interval of time.
For a large data set, however, this can use an inordinate amount of
storage and/or compute time. If the times spacing is more coarse than
this, an actuarial approximation is used which should, however, be extremely
accurate as long as all of the returned values are > .99.
For a subgroup of size 1 and times > y,
the conditional method reduces to exp(-h) where
h is the expected cumulative hazard for the subject over his/her
observation time. This is used to compute individual expected survival.
survexp, survexp.us