sm.survival {sm} | R Documentation |
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.
sm.survival(x, y, status, h , hv = 0.05, p = 0.5, status.code = 1, ...)
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:
|
see Section 3.5 of the reference below.
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.
none.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
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)