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Last data update: 2014.03.03

R: Calculates GoF measures for Cox's propoportional hazard model...

cumres.coxph

R Documentation

Calculates GoF measures for Cox's propoportional hazard model for right censored survival times

Description

Calculates score processes and KS and Cvm tests for proportionaly of hazards via simulation (Martinussen and Scheike, 2006).

Usage

  ## S3 method for class 'coxph'
 cumres(model,
    variable = c(colnames(model.matrix(model))),
    type = c("score", "residual"), R = 1000,
    plots = min(R, 50), seed = round(runif(1, 1, 1e+09)),
    ...)

Arguments

`model`	Model object (`lm` or `glm`)
`variable`	List of variable to order the residuals after
`R`	Number of samples used in simulation
`type`	Type of GoF-procedure
`plots`	Number of realizations to save for use in the plot-routine
`seed`	Random seed
`...`	additional arguments

Value

Returns an object of class 'cumres'.

Author(s)

Klaus K. Holst and Thomas Scheike

References

Lin, D. Y. and Wei, L. J. and Ying, Z. (1993) Checking the Cox model with cumulative sums of martingale-based residuals Biometrika, Volume 80, No 3, p. 557-572.

Martinussen, Torben and Scheike, Thomas H. Dynamic regression models for survival data (2006), Springer, New York.

Examples

library(survival)

simcox <- function(n=100, seed=1) {
  if (!is.null(seed))
    set.seed(seed)
  require(survival)
  time<-rexp(n); cen<-2*rexp(n);
  status<-(time<cen);
  time[status==0]<-cen[status==0];
  X<-matrix(rnorm(2*n),n,2)
  return(data.frame(time=time, status=status, X))
}
n <- 100; d <- simcox(n); m1 <- coxph(Surv(time,status)~ X1 + X2, data=d)
cumres(m1)

## Not run: 
## PBC example
data(pbc)
fit.cox <- coxph(Surv(time,status==2) ~ age + edema + bili + protime, data=pbc)
system.time(pbc.gof <- cumres(fit.cox,R=2000))
par(mfrow=c(2,2))
plot(pbc.gof, ci=TRUE, legend=NULL)

## End(Not run)