R Under development (unstable) (2024-06-02 r86665 ucrt) -- "Unsuffered Consequences"
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> library(survival)
> options(na.action=na.exclude) # preserve missings
> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
> aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
> 
> #
> # Tests from the appendix of Therneau and Grambsch
> #  a. Data set 1 and Breslow estimate
> #  The data below is not in time order, to also test sorting, and has 1 NA
> #
> test1 <- data.frame(time=  c(9, 3,1,1,6,6,8),
+                     status=c(1,NA,1,0,1,1,0),
+                     x=     c(0, 2,1,1,1,0,0))
> 
> # Nelson-Aalen influence
> s1 <- survfit(Surv(time, status) ~1, test1, id=1:7, influence=TRUE)
> inf1 <- matrix(c(10, rep(-2,5), 10, -2, 7,7, -11, -11)/72,
+                ncol=2)
> indx <- order(test1$time[!is.na(test1$status)])
> aeq(s1$influence.chaz[indx,], inf1[,c(1,2,2,2)])
[1] TRUE
> 
> # KM influence
> inf2 <- matrix(c(-20, rep(4,5), -10, 2, -13, -13, 17, 17,
+                  rep(0,6))/144, ncol=3)
> aeq(s1$influence.surv[indx,], inf2[, c(1,2,2,3)])
[1] TRUE
> 
> # Fleming-Harrington influence
> s2 <- survfit(Surv(time, status) ~ 1, test1, id=1:7, ctype=2, influence=2)
> inf3 <- matrix(c( rep(c(5, -1), c(1, 5))/36, c(5,-1)/36, 
+                  c(21,21,-29, -29)/144), ncol=2)
> aeq(s2$influence.chaz[indx,], inf3[,c(1,2,2,2)])
[1] TRUE
> 
> 
> # Breslow estimate
> byhand1 <- function(beta, newx=0) {
+     r <- exp(beta)
+     loglik <- 2*beta - (log(3*r+3) + 2*log(r+3))
+     u <- (6 + 3*r - r^2) / ((r+1)*(r+3))
+     imat <- r/(r+1)^2 + 6*r/(r+3)^2
+ 
+     x <- c(1,1,1,0,0,0)
+     status <- c(1,0,1,1,0,1)
+     xbar <- c(r/(r+1), r/(r+3), 0, 0)  # at times 1, 6, 8 and 9
+     haz <- c(1/(3*r+3), 2/(r+3), 0, 1 )
+     ties <- c(1,1,2,2,3,4)
+     wt <- c(r,r,r,1,1,1)
+     mart <- c(1,0,1,1,0,1) -  wt* (cumsum(haz))[ties]  #martingale residual
+ 
+     a <- 3*(r+1)^2; b<- (r+3)^2
+     score <- c((2*r+3)/a, -r/a, -r/a + 3*(3-r)/b,  r/a - r*(r+1)/b,
+                r/a + 2*r/b, r/a + 2*r/b)
+ 
+     # Schoenfeld residual
+     scho <- c(1/(r+1), 1- (r/(3+r)), 0-(r/(3+r)) , 0)
+ 
+     surv  <- exp(-cumsum(haz)* exp(beta*newx))
+     varhaz.g <- cumsum(c(1/(3*r+3)^2, 2/(r+3)^2, 0, 1 ))
+ 
+     varhaz.d <- cumsum((newx-xbar) * haz)
+ 
+     varhaz <- (varhaz.g + varhaz.d^2/ imat) * exp(2*beta*newx)
+ 
+     names(xbar) <- names(haz) <- 1:4
+     names(surv) <- names(varhaz) <- 1:4
+     list(loglik=loglik, u=u, imat=imat, xbar=xbar, haz=haz,
+ 	     mart=mart,  score=score,
+ 		scho=scho, surv=surv, var=varhaz,
+ 		varhaz.g=varhaz.g, varhaz.d=varhaz.d)
+     }
> 
> 
> 
> fit0 <-coxph(Surv(time, status) ~x, test1, iter=0, method='breslow')
> truth0 <- byhand1(0,0)
> aeq(truth0$loglik, fit0$loglik[1])
[1] TRUE
> aeq(1/truth0$imat, fit0$var)
[1] TRUE
> aeq(truth0$mart, fit0$residuals[c(2:6,1)])
[1] TRUE
> aeq(truth0$scho, resid(fit0, 'schoen'))
[1] TRUE
> aeq(truth0$score, resid(fit0, 'score')[c(3:7,1)])
[1] TRUE
> sfit <- survfit(fit0, list(x=0))
> aeq(sfit$cumhaz, cumsum(truth0$haz))
[1] TRUE
> aeq(sfit$surv, exp(-cumsum(truth0$haz)))
[1] TRUE
> aeq(sfit$std.err^2, c(7/180, 2/9, 2/9, 11/9))
[1] TRUE
> aeq(resid(fit0, 'score'), c(5/24, NA, 5/12, -1/12, 7/24, -1/24, 5/24))
[1] TRUE
> 
> fit1 <- coxph(Surv(time, status) ~x, test1, iter=1, method='breslow')
> aeq(fit1$coefficients, 8/5)
[1] TRUE
> 
> # This next gives an ignorable warning message
> fit2 <- coxph(Surv(time, status) ~x, test1, method='breslow', iter=2)
Warning message:
In coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not converge
> aeq(round(fit2$coefficients, 6), 1.472724)
[1] TRUE
> 
> fit <- coxph(Surv(time, status) ~x, test1, method='breslow', eps=1e-8,
+              nocenter=NULL)
> aeq(fit$coefficients, log(1.5 + sqrt(33)/2))  # the true solution
[1] TRUE
> truth <- byhand1(fit$coefficients, 0)
> aeq(truth$loglik, fit$loglik[2])
[1] TRUE
> aeq(1/truth$imat, fit$var)
[1] TRUE
> aeq(truth$mart, fit$residuals[c(2:6,1)])
[1] TRUE
> aeq(truth$scho, resid(fit, 'schoen'))
[1] TRUE
> aeq(truth$score, resid(fit, 'score')[c(3:7,1)])
[1] TRUE
> expect <- predict(fit, type='expected', newdata=test1) #force recalc
> aeq(test1$status[-2] -fit$residuals, expect[-2]) #tests the predict function
[1] TRUE
> 
> sfit <- survfit(fit, list(x=0), censor=FALSE)
> aeq(sfit$std.err^2, truth$var[c(1,2,4)]) # sfit skips time 8 (no events there)
[1] TRUE
> aeq(-log(sfit$surv), (cumsum(truth$haz))[c(1,2,4)])
[1] TRUE
> sfit <- survfit(fit, list(x=0), censor=TRUE)
> aeq(sfit$std.err^2, truth$var) 
[1] TRUE
> aeq(-log(sfit$surv), (cumsum(truth$haz)))
[1] TRUE
> 
> # 
> # Done with the formal test, now print out lots of bits
> #
> resid(fit)
         1          2          3          4          5          6          7 
-0.3333333         NA  0.7287136 -0.2712864 -0.4574271  0.6666667 -0.3333333 
> resid(fit, 'scor')
          1           2           3           4           5           6 
 0.21138938          NA  0.13564322 -0.05049744 -0.12624360 -0.38168095 
          7 
 0.21138938 
> resid(fit, 'scho')
         1          6          6          9 
 0.1861407  0.4069297 -0.5930703  0.0000000 
> 
> predict(fit, type='lp', se.fit=T)
$fit
         1          2          3          4          5          6          7 
-0.7376425         NA  0.7376425  0.7376425  0.7376425 -0.7376425 -0.7376425 

$se.fit
        1         2         3         4         5         6         7 
0.6278672        NA 0.6278672 0.6278672 0.6278672 0.6278672 0.6278672 

> predict(fit, type='risk', se.fit=T)
$fit
        1         2         3         4         5         6         7 
0.4782401        NA 2.0910001 2.0910001 2.0910001 0.4782401 0.4782401 

$se.fit
        1         2         3         4         5         6         7 
0.4342009        NA 0.9079142 0.9079142 0.9079142 0.4342009 0.4342009 

> predict(fit, type='expected', se.fit=T)
$fit
        1         2         3         4         5         6         7 
1.3333333        NA 0.2712864 0.2712864 1.4574271 0.3333333 0.3333333 

$se.fit
[1] 1.0540926        NA 0.2785989 0.2785989 1.1069433 0.3333333 0.3333333

> predict(fit, type='terms', se.fit=T)
$fit
           x
1 -0.7376425
2         NA
3  0.7376425
4  0.7376425
5  0.7376425
6 -0.7376425
7 -0.7376425

$se.fit
          x
1 0.6278672
2        NA
3 0.6278672
4 0.6278672
5 0.6278672
6 0.6278672
7 0.6278672

> 
> summary(survfit(fit, list(x=2)))
Call: survfit(formula = fit, newdata = list(x = 2))

 time n.risk n.event survival  std.err lower 95% CI upper 95% CI
    1      6       1 3.05e-01 6.50e-01     4.72e-03            1
    6      4       2 1.71e-03 1.98e-02     2.33e-13            1
    9      1       1 8.52e-12 5.29e-10     1.22e-64            1
> 
> proc.time()
   user  system elapsed 
   0.92    0.14    1.01