rsq {survMisc}

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(\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

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