survMisc (version 0.5.6)

rsq: 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 coxph
rsq(x, ..., sigD = 2)
# S3 method for 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 $$R^2=1-\exp(\frac{2}{n}L_0-L_1)$$ 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: $$R^2_{mer}=1-\exp(\frac{2}{m}L_0-L_1)$$ where \(m\) is the number of observed events.

mev

The measure of explained variation (similar to that for linear regression), which is: $$R^2=\frac{R^2_{mer}}{R^2_{mer} + \frac{\pi}{6}(1-R^2_{mer})}$$

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

# NOT RUN {
data("kidney", package="KMsurv")
c1 <- coxph(Surv(time=time, event=delta) ~ type, data=kidney)
cbind(rsq(c1), rsq(c1, sigD=NULL))

# }