R version 4.4.0 RC (2024-04-16 r86458 ucrt) -- "Puppy Cup"
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> #
> # 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])
[1] TRUE
> all.equal(c(fit1$residuals,0), fit2$residuals, check.attributes=FALSE)
[1] TRUE
> 
> # 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$coef)
> 
> all.equal(fit3[ii], fit4[ii])
[1] TRUE
> all.equal(c(fit3$residuals,0),  fit4$residuals, check.attributes=FALSE)
[1] TRUE
> all.equal(fit4$residuals, fit5$residuals)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   0.96    0.07    1.04