# # Make sure that useless intervals do not cause issues, i.e., any that do # not overlap at least one event time # library(survival) test2 <- data.frame(time1 =c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8, 3), time2 =c(2, 3, 6, 7, 8, 9, 9, 9,14,17, 5), event =c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0), x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 500) ) # The data set is the same as book3.R, except for the wild observation # with x=500 whose time interval of (4,5) overlaps no events. fit1 <- coxph(Surv(time1, time2, event) ~ x, test2, subset=(x<100)) fit2 <- coxph(Surv(time1, time2, event) ~ x, test2) ii <- match(c("coefficients", "var", "loglik", "score", "iter", "wald.test", "concordance"), names(fit1)) all.equal(fit1[ii], fit2[ii]) all.equal(c(fit1$residuals,0), fit2$residuals, check.attributes=FALSE) # The mean differs condiderably, and so to the linear predictors # Now the same with a penalized model fit3 <- coxph(Surv(time1, time2, event) ~ ridge(x, theta=.1), test2, subset= (x< 100)) fit4 <- coxph(Surv(time1, time2, event) ~ ridge(x, theta=.1), test2) fit5 <- coxph(Surv(time1,time2, event) ~ x, test2, iter=0, init=fit4$coef) all.equal(fit3[ii], fit4[ii]) all.equal(c(fit3$residuals,0), fit4$residuals, check.attributes=FALSE) all.equal(fit4$residuals, fit5$residuals)