plot.scoreproptest {proptest} | R Documentation |
The function plots the observed test process and a number of its realisations simulated under the hypothesis of proportional hazards.
## S3 method for class 'scoreproptest': plot(x, nsim.plot = x$nsim.plot, ...)
x |
an object of class "scoreproptest" (output of
scoreproptest ). |
nsim.plot |
the number of simulated paths of the test process
to be plotted. It must not be greater than x$nsim.plot . |
... |
further plotting parameters. |
By plotting the observed path of the test process along with its simulations, one can visually assess the time-constancy of the effect of the corresponding covariate or the global proportional hazards assumption.
The function plots x$score.process
or x$test.process
and the first nsim.plot
realisations contained in
x$score.process.sim
or x$test.process.sim
.
David Kraus, http://www.davidkraus.net/
Lin, D.Y., Wei, L.J. and Ying, Z. (1993) Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika, 80, 557–572.
## Case 4 of Kvaloy & Neef (2004, Lifetime Data Anal.): ## data generated from the distribution with hazard rate ## \lambda(t)=\exp(0.5tZ_1+Z_2-8) ## (Z_1,Z_2) jointly normal with E=4, var=1, cor=rho ## censoring times uniform(0,5) n = 200 rho = .3 z = matrix(rnorm(n*2),ncol=2) %*% chol(matrix(c(1,rho,rho,1),2)) + 4 a = .5 tim = 1/(a*z[,1]) * log(1-a*z[,1]*exp(-z[,2]+8)*log(runif(n))) ct = 5*runif(n) nc = tim<=ct tim = pmin(tim,ct) fit = coxph(Surv(tim,nc)~z) par(mfrow=c(3,1)) test1 = scoreproptest(fit,covariate=1) # testing Z_1 (nonproportional) print(test1) plot(test1,main="Score process for z1") test2 = scoreproptest(fit,covariate=2) # testing Z_2 (proportional) print(test2) plot(test2,main="Score process for z2") test = scoreproptest(fit,global=TRUE) # global test of proportional hazards print(test) plot(test,main="Global test process") par(mfrow=c(1,1))