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.
sm.survival(x, y, status, h , hv = 0.05, p = 0.5, status.code = 1, ...)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.
a vector of covariate values.
a vector of survival times.
an indicator of a complete survival time or a censored value. The value of
status.code defines a complete survival time.
the smoothing parameter applied to the covariate scale. A normal kernel
function is used and h is its standard deviation.
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.
the quantile to be estimated at each covariate value.
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.
a plot on the current graphical device is produced, unless the option
display="none" is set.
see Section 3.5 of the reference below.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.regression, sm.options
x <- runif(50, 0, 10)
y <- rexp(50, 2)
z <- rexp(50, 1)
status <- rep(1, 50)
status[zRun the code above in your browser using DataLab