tests/nest1.R

In coxme: Mixed Effects Cox Models

library(coxme)
options(na.action='na.exclude', contrasts=c('contr.treatment', 'contr.poly'))
aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
#
# Very simple data set, to test out nested factors
#
# The first version of coxme put f1 before f1/f2, the current does
#  the opposite
# old: indx <- 1:10
indx <- c(4:9, 1:3, 10)

# Undo a gchol: given gchol(x), returns x
#
igchol <- function(x) {
    dd <- diag(x)
    ll <- as.matrix(x)
    ll %*% diag(dd) %*% t(ll)
    }

simple <- data.frame(time=c(9:3,1,1), status=c(rep(0,7),1,1),
                     f1=rep(1:3,3), f2=c(rep(1,6), rep(2,3)), x=1:9)
sfit <- coxme(Surv(time, status) ~ x + (1| f1/f2), data=simple, ties='breslow',
               vfixed=c(1,2), varlist=list(coxmeFull(collapse=F)), iter=0)

ta <- 2/9
tb <- -1/9
tc <- 4/27
td <- 14/81 
te <- 8/81 
tf <- -2/27
tg <- 2/27
th <- -1/27
ti <- -2/81
tj <- -4/81
tk <- -1/81
tx <- c(-3,0,3,-5,2,-3,3,-1,4,60)/9
itrue <- bdsmatrix(c(ta, tb, tb, tc, tg, tf, th, tf, th,
                         ta, tb, tf, th, tc, tg, tf, th,
                             ta, tf, th, tf, th, tc, tg,
                                 td, ti, tj, ti, tj, ti,
                                     te, ti, tk, ti, tk,
                                         td, ti, tj, ti,
                                             te, ti, tk,
                                                 td, ti, te),
                   blocksize=9, rmat=as.matrix(tx))
ibreslow <- 2*itrue[indx, indx]
diag(ibreslow) <- diag(ibreslow) + rep(c(1,1/2,0), c(6,3,1))

aeq(as.matrix(igchol(sfit$hmat)), as.matrix(ibreslow))


sfit2 <- coxme(Surv(time, status) ~ x + (1| f1/f2), 
               data=simple, ties='breslow',  varlist=coxmeFull(collapse=F),
              vfixed=c(1,2), iter=0, sparse.calc=1)
aeq(as.matrix(igchol(sfit2$hmat)), as.matrix(ibreslow))

# Check the collapse option
sfit2 <- coxme(Surv(time, status) ~ x + (1| f1/f2), 
               data=simple, ties='breslow',  varlist=coxmeFull(collapse=F),
              vfixed=c(1,2))
sfit3 <- coxme(Surv(time, status) ~ x + (1| f1/f2), 
               data=simple, ties='breslow',  varlist=coxmeFull(collapse=T),
              vfixed=c(1,2))
aeq(sfit2$log, sfit3$log)
aeq(fixef(sfit2), fixef(sfit3))
aeq(unlist(ranef(sfit3)), ranef(sfit2)[[1]] +ranef(sfit2)[[2]][c(1,1,2,2,3,3)])

# Now for the Efron approx
sfit <- coxme(Surv(time, status) ~ x + (1| f1/f2), data=simple, 
              ties='efron', varlist=coxmeFull(collapse=FALSE),
              vfixed=c(1,2), iter=0)

# the matrix for the first death, where each of the last 2 obs has weight
#  1/2
tx <- c(-54, -13, 67, -132, 78, -68, 55, -4, 71, 1471)
i2 <- bdsmatrix(c( 60, -30, -30,  40,  20, -24,  -6, -24,  -6, 
                        55, -25, -20, -10,  44,  11, -20,  -5, 
                             55, -20, -10, -20,  -5,  44,  11, 
                                  48,  -8, -16,  -4, -16,  -4, 
                                       28,  -8,  -2,  -8,  -2, 
                                            48,  -4, -16,  -4, 
                                                 15,  -4,  -1, 
                                                      48,  -4,     
		                                           15),
                   blocksize=9, rmat=as.matrix(tx))
iefron <- (itrue + i2/256)[indx, indx]
diag(iefron) <- diag(iefron) +  rep(c(1,1/2,0), c(6,3,1)) #add penalty
aeq(as.matrix(igchol(sfit$hmat)), as.matrix(iefron))

sfit2 <- coxme(Surv(time, status) ~ x + (1| f1/f2), data=simple, ties='efron', 
              varlist=coxmeFull(collapse=FALSE), vfixed=c(1,2), 
               iter=0, sparse.calc=1)
aeq(as.matrix(igchol(sfit2$hmat)), as.matrix(iefron))