plot.scoreproptest {proptest}R Documentation

Plotting the Observed Score Process and its Simulations

Description

The function plots the observed score process and a number of its realisations simulated under the hypothesis of proportional hazards.

Usage

plot.scoreproptest(x, nsim.plot = x$nsim.plot, ...)

Arguments

x an object of class "scoreproptest" (output of scoreproptest).
nsim.plot the number of simulated paths of the score process to be plotted. It must not be greater than x$nsim.plot.
... further plotting parameters.

Details

By plotting the observed path of the score process along with its simulations, one can visually assess the time-constancy of the effect of the corresponding covariate.

The function plots x$score.process and the first nsim.plot realisations contained in x$score.process.sim.

Author(s)

David Kraus, http://www.davidkraus.net/

References

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.

See Also

scoreproptest

Examples

## 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(2,1))

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")

par(mfrow=c(1,1))

[Package proptest version 0.1-0 Index]