R version 4.4.0 beta (2024-04-12 r86413 ucrt) -- "Puppy Cup"
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> library(survival)
> #
> # Tests with the pspline function, to verify the prediction aspects
> #
> options(na.action=na.exclude)
> aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
> 
> spfit <- coxph(Surv(time, status) ~ pspline(age) + ph.ecog, lung)
> 
> spfit2 <- coxph(Surv(time, status) ~ pspline(age) + ph.ecog, lung, x=TRUE)
> x2 <- model.matrix(spfit)
> all.equal(spfit2$x, x2)
[1] TRUE
> 
> keep <- (lung$age < 60)
> x3 <- model.matrix(spfit, data=lung[keep,])  
> attr(x3, 'assign') <- NULL #subscripting loses the assign attr below
> all.equal(napredict(spfit$na.action,x2)[keep,], x3)
[1] TRUE
> 
> p2 <- predict(spfit, newdata=lung[keep,])
> aeq(p2, predict(spfit)[keep])
[1] TRUE
> 
> 
> p3 <- survfit(spfit)
> p4 <- survfit(spfit, newdata=lung[1:2,])
> temp <- scale(x2[1:2,], center=spfit$means, scale=FALSE)%*% coef(spfit)
> aeq(p3$time, p4$time)
[1] TRUE
> aeq(outer(-log(p3$surv), exp(temp), '*'), -log(p4$surv))
[1] TRUE
> 
> # Check out model.frame
> spfit3 <- coxph(Surv(time, status) ~ pspline(age) + sex, lung,
+                 model=TRUE)  #avoid the missing value
> m2 <- model.frame(spfit3, data=lung[keep,])
> all.equal(m2, spfit3$model[keep,], check.attributes=FALSE)
[1] TRUE
> 
> #
> # Test of residuals, in response to a reported bug.  
> # These are three progam paths that should all lead to the same C routine
> fit <- coxph(Surv(tstart, tstop, status) ~ sex + treat + pspline(age), cgd)
> fit2 <- coxph(Surv(tstart, tstop, status) ~ fit$linear, cgd, iter=0, init=1)
> fit3 <- coxph(Surv(tstart, tstop, status) ~ offset(fit$linear), cgd)
> all.equal(fit$resid, fit2$resid)
[1] TRUE
> all.equal(fit$resid, fit3$resid)
[1] TRUE
> 
> # 
> # Check using coxph.detail. The matrix multiply below only is
> #  valid for the breslow approximation.
> fit4 <-  coxph(Surv(tstart, tstop, status) ~ sex + treat + pspline(age),
+                cgd, ties='breslow')
> dt <- coxph.detail(fit4, riskmat=TRUE)
> 
> # the results of coxph.detail used to be in time order, now are in data set
> #  order
> rscore <- exp(fit4$linear)
> exp4 <- (rscore *dt$riskmat) %*% dt$hazard
> r4 <- cgd$status - exp4
> aeq(r4, fit4$resid)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   0.81    0.14    0.93