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

Description Usage Arguments Value References Examples

Description

r^2 measures for a a coxph or survfit model

Usage

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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.

sigD

significant digits (for ease of display). If sigD=NULL, will return the original numbers.

...

Additional arguments (not implemented).

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. JSTOR

O'Quigley J, Xu R, Stare J, 2005. Explained randomness in proportional hazards models. Stat Med 24(3):479–89. Wiley (paywall) Available at UCSD

Royston P, 2006. Explained variation for survival models. The Stata Journal 6(1):83–96. The Stata Journal

Examples

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

dardisco/survMisc documentation built on May 14, 2019, 6:08 p.m.