sm (version 2.0-14)

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 Kaplan-Meier 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 Kaplan-Meier functions 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 the following:

• eval.points

{ the points a

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

none.

Details

see Section 3.5 of the reference below.

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)