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