R: Nonparametric regression with survival data.

sm.survival {sm} | R Documentation

## Nonparametric regression with survival data.

### Description

This function creates a smooth, nonparametric estimate of the quantile of the distribution of survival data as a function of a single covariate. A weighted product-limit estimate of the survivor function is obtained by smoothing across the covariate scale. A small amount of smoothing is then also applied across the survival time scale in order to achieve a smooth estimate of the quantile.

### Usage

    sm.survival(x, y, status, h , hv = 0.05, p = 0.5, status.code = 1, ...)

### Arguments

`x` | a vector of covariate values.
`y` | a vector of survival times.
`status` | an indicator of a complete survival time or a censored value. The value of `status.code` defines a complete survival time.
`h` | the smoothing parameter applied to the covariate scale. A normal kernel function is used and `h` is its standard deviation.
`hv` | a smoothing parameter applied to the weighted to the product-limit estimate derived from the smoothing procedure in the covariate scale. This ensures that a smooth estimate is obtained.
`p` | the quantile to be estimated at each covariate value.
`status.code` | the value of `status` which defines a complete survival time.
`...` | other optional parameters are passed to the `sm.options` function, through a mechanism which limits their effect only to this call of the function; those relevant for this function are `add`, `eval.points`, `ngrid`, `display`, `xlab`, `ylab`, `lty`; see the documentation of `sm.options` for their description.

### Details

see Section 3.5 of the reference below.

### Value

a list containing the values of the estimate at the evaluation points and the values of the smoothing parameters for the covariate and survival time scales.

### Side Effects

a plot on the current graphical device is produced, unless the option `display="none"` is set.

### References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

### See Also

`sm.regression`, `sm.options`

### Examples

    x <- runif(50, 0, 10)
    y <- rexp(50, 2)
    z <- rexp(50, 1)
    status <- rep(1, 50)
    status[z<y] <- 0
    y <- pmin(z, y)
    sm.survival(x, y, status, h=2)

[Package sm version 2.2-5.4 Index]