sm: sm.survival – R documentation

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sm.survival

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.

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)

sm

Smoothing Methods for Nonparametric Regression and Density Estimation

v2.2-5.6

GPL (>= 2)

Authors

Adrian Bowman and Adelchi Azzalini. Ported to R by B. D. Ripley <ripley@stats.ox.ac.uk> up to version 2.0, version 2.1 by Adrian Bowman and Adelchi Azzalini, version 2.2 by Adrian Bowman.

Initial release

2018-09-27