km.mrl {locfit} | R Documentation |

## Mean Residual Life using Kaplan-Meier estimate

### Description

This function computes the mean residual life for censored data
using the Kaplan-Meier estimate of the survival function. If
`S(t)`

is the K-M estimate, the MRL for a censored observation
is computed as `(\int_t^{\infty} S(u)du)/S(t)`

. We take
`S(t)=0`

when `t`

is greater than the largest observation,
regardless of whether that observation was censored.

When there are ties between censored and uncensored observations, for definiteness our ordering places the censored observations before uncensored.

This function is used by `locfit.censor`

to compute
censored regression estimates.

### Usage

```
km.mrl(times, cens)
```

### Arguments

`times` |
Obsereved survival times. |

`cens` |
Logical variable indicating censoring. The coding is |

### Value

A vector of the estimated mean residual life. For uncensored observations, the corresponding estimate is 0.

### References

Buckley, J. and James, I. (1979). Linear Regression with censored data. Biometrika 66, 429-436.

Loader, C. (1999). Local Regression and Likelihood. Springer, NY (Section 7.2).

### See Also

### Examples

```
# censored regression using the Kaplan-Meier estimate.
data(heart, package="locfit")
fit <- locfit.censor(log10(surv+0.5)~age, cens=cens, data=heart, km=TRUE)
plotbyfactor(heart$age, 0.5+heart$surv, heart$cens, ylim=c(0.5,16000), log="y")
lines(fit, tr=function(x)10^x)
```

*locfit*version 1.5-9.10 Index]