# Compute Expected Survival

### Description

Compute expected survival times.

### Usage

1 | ```
survexp.fit(group, x, y, times, death, ratetable)
``` |

### Arguments

`group` |
if there are multiple survival curves this identifies the group, otherwise it is a constant. Must be an integer. |

`x` |
A matrix whose columns match the dimensions of the |

`y` |
the follow up time for each subject. |

`times` |
the vector of times at which a result will be computed. |

`death` |
a logical value, if |

`ratetable` |
a rate table, such as |

### Details

For conditional survival `y`

must be the time of last follow-up or death for
each subject.
For cohort survival it must be the potential censoring time for
each subject, ignoring death.

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.

### Value

A list containing the number of subjects and the expected survival(s) at each time point. If there are multiple groups, these will be matrices with one column per group.

### Warning

Most users will call the higher level routine `survexp`

.
Consequently, this function has very few error checks on its input arguments.

### See Also

`survexp`

, `survexp.us`

.