R Under development (unstable) (2024-03-03 r86036 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(coxme)
Loading required package: survival
Loading required package: bdsmatrix

Attaching package: 'bdsmatrix'

The following object is masked from 'package:base':

    backsolve

> options(na.action='na.exclude', contrasts=c('contr.treatment', 'contr.poly'))
> aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
> 
> #
> # Now do iterations on tdata0, checking it out with coxph.detail
> #   Note that the coxph calls give some "singular matrix" warnings
> #   which can be ignored
> #
> tdata0 <- data.frame(time  =c(5,4,1,1,2,2,2,2,3), 
+                      status=c(0,1,1,0,1,1,1,0,0),
+                      x1    =c(0,1,2,0,1,1,0,1,0),
+                      wt    =c(1,2,1,2,3,4,3,2,1),
+                      x2    =c(1,3,5,2,3,6,4,3,1),
+                      grp   =c(1,1,2,2,1,1,2,2,1))
> theta <- .53
> fit0 <- coxme(Surv(time, status) ~ x1 + x2 + (1|grp), data=tdata0,
+               vfixed=theta, weight=wt, iter=0)
> 
> tfit <- coxph(Surv(time, status) ~ I(grp==1) + I(grp==2) + x1 + x2,
+ 	      data=tdata0, x=T, weight=wt, iter=0)
> dt0 <- coxph.detail(tfit)
> 
> aeq(apply(dt0$score,2,sum), fit0$u)
[1] TRUE
> h0 <- apply(dt0$imat,1:2,sum) + diag(c(1/theta, 1/theta,0,0))
> aeq(as.matrix(gchol(h0)), as.matrix(fit0$hmat))
[1] TRUE
> aeq(as.matrix(solve(fit0$var, full=F)), h0)
[1] TRUE
> 
> # Now iteration 1
> fit1 <- coxme(Surv(time, status) ~ x1 + x2 +(1|grp), data=tdata0,
+               vfixed=theta, weight=wt, iter=1)
> aeq(fit0$u %*% fit0$var, c(unlist(ranef(fit1)), fixef(fit1)))
[1] TRUE
> tfit <- coxph(Surv(time, status) ~ I(grp==1) + I(grp==2) + x1 + x2,
+ 	      data=tdata0, x=T, weight=wt, iter=0,
+ 	      init=c(unlist(ranef(fit1)), fixef(fit1)))
> dt1 <- coxph.detail(tfit)
> 
> aeq(apply(dt1$score,2,sum)- c(unlist(ranef(fit1)), 0,0)/theta, fit1$u)
[1] TRUE
> h1 <- apply(dt1$imat,1:2,sum) + diag(c(1/theta, 1/theta,0,0))
> aeq(as.matrix(gchol(h1)), as.matrix(fit1$hmat))
[1] TRUE
> aeq(as.matrix(solve(fit1$var, full=F)), h1)
[1] TRUE
> 
> # And iteration 2
> fit2 <- coxme(Surv(time, status) ~ x1 + x2 + (1|grp), data=tdata0,
+               vfixed=theta, weight=wt, iter=2)
> aeq(solve(fit1$hmat, fit1$u), 
+     c(unlist(ranef(fit2)), fixef(fit2)) - 
+     c(unlist(ranef(fit1)), fixef(fit1)))
[1] TRUE
> aeq(solve(fit1$hmat, fit1$u), fit1$variance %*% fit1$u)
[1] TRUE
> 
> #
> # a copy of tdata0, but with several subjects broken into multiple pieces,
> #  exercises the "add in & take out" portions of the code
> #
> rcnt <- c(3,2,1,1,1,2,2,1,3)  # rep count
> tdata0b <- data.frame(time2  = c(3,4,5, 2,4, 1,1,2, 1,2, .5,2, 2, 1,2,3), 
+ 		      time1  = c(0,3,4, 0,2, 0,0,0, 0,1, 0,.5, 0, 0,1,2),
+ 		      status = c(0,0,0, 0,1, 1,0,1, 0,1, 0, 1, 0, 0,0,0),
+                       x1     = rep(tdata0$x1, rcnt),
+                       wt     = rep(tdata0$wt, rcnt),
+                       x2     = rep(tdata0$x2, rcnt),
+                       grp    = rep(tdata0$grp, rcnt))
> fit0 <- coxme(Surv(time1, time2, status) ~ x1 + x2 + (1|grp), data=tdata0b,
+               vfixed=theta, weight=wt, iter=0)
> 
> tfit <- coxph(Surv(time1, time2, status) ~ I(grp==1) + I(grp==2) + x1 + x2,
+ 	      data=tdata0b, x=T, weight=wt, iter=0)
> dt0 <- coxph.detail(tfit)
> 
> aeq(apply(dt0$score,2,sum), fit0$u)
[1] TRUE
> h0 <- apply(dt0$imat,1:2,sum) + diag(c(1/theta, 1/theta,0,0))
> aeq(as.matrix(gchol(h0)), as.matrix(fit0$hmat))
[1] TRUE
> aeq(as.matrix(solve(fit0$var, full=F)), h0)
[1] TRUE
> 
> # Now iteration 1
> fit1 <- coxme(Surv(time1, time2, status) ~ x1 + x2 + (1|grp), data=tdata0b,
+               vfixed=theta, weight=wt, iter=1)
> aeq(fit0$u %*% fit0$var, c(unlist(ranef(fit1)), fixef(fit1)))
[1] TRUE
> tfit <- coxph(Surv(time1, time2, status) ~ I(grp==1) + I(grp==2) + x1 + x2,
+ 	      data=tdata0b, x=T, weight=wt, iter=0,
+ 	      init=c(unlist(ranef(fit1)), fixef(fit1)))
> dt1 <- coxph.detail(tfit)
> 
> aeq(apply(dt1$score,2,sum)- c(unlist(ranef(fit1)), 0,0)/theta, fit1$u)
[1] TRUE
> h1 <- apply(dt1$imat,1:2,sum) + diag(c(1/theta, 1/theta,0,0))
> aeq(as.matrix(gchol(h1)), as.matrix(fit1$hmat))
[1] TRUE
> aeq(as.matrix(solve(fit1$var, full=F)), h1)
[1] TRUE
> 
> # And iteration 2
> fit2 <- coxme(Surv(time1, time2, status) ~ x1 + x2 + (1|grp), data=tdata0b,
+               vfixed=theta, weight=wt, iter=2)
> aeq(fit1$u %*% fit1$var, c(unlist(ranef(fit2)), fixef(fit2)) - 
+                          c(unlist(ranef(fit1)), fixef(fit1)))
[1] TRUE
> 
> rm(fit0, fit1, dt0, dt1, tfit, h0, h1, theta)
> rm(fit2)
> 
> proc.time()
   user  system elapsed 
   1.51    0.18    1.64