rsq: r^2 measures for a a 'coxph' or 'survfit' model

In survMisc: Miscellaneous Functions for Survival Data

r^2 measures for a a coxph or survfit model

Description

r^2 measures for a a coxph or survfit model

Usage

rsq(x, ...)

## S3 method for class 'coxph'
rsq(x, ..., sigD = 2)

## S3 method for class 'survfit'
rsq(x, ..., sigD = 2)

Arguments

x	A survfit or coxph object.
...	Additional arguments (not implemented).
sigD	significant digits (for ease of display). If sigD=NULL, will return the original numbers.

Value

A list with the following elements:

cod	The coefficient of determination, which is R^2 = 1-exp((2/n).(L[0]-L[1])) where L[0] and L[1] are the log partial likelihoods for the null and full models respectively and n is the number of observations in the data set.
mer	The measure of explained randomness, which is: R^2[mer] = 1-exp((2/m).(L[0]-L[1])) where m is the number of observed events.
mev	The measure of explained variation (similar to that for linear regression), which is: R^2 = R^2[mer] / ( R^2[mer] + pi/6(1-R^2[mer]) )

References

Nagelkerke NJD, 1991. A Note on a General Definition of the Coefficient of Determination. Biometrika 78(3):691–92. http://www.jstor.org/stable/2337038 JSTOR

O'Quigley J, Xu R, Stare J, 2005. Explained randomness in proportional hazards models. Stat Med 24(3):479–89. http://dx.doi.org/10.1002/sim.1946 Wiley (paywall) http://www.math.ucsd.edu/~rxu/igain2.pdf UCSD (free)

Royston P, 2006. Explained variation for survival models. The Stata Journal 6(1):83–96. http://www.stata-journal.com/sjpdf.html?articlenum=st0098

Examples

data("kidney", package="KMsurv")
c1 <- coxph(Surv(time=time, event=delta) ~ type, data=kidney)
cbind(rsq(c1), rsq(c1, sigD=NULL))

survMisc documentation built on April 7, 2022, 5:06 p.m.