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

rsq {survMisc} | R Documentation

## 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.
`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

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

[Package survMisc version 0.5.4 Index]