### R code from vignette source 'methods.Rnw'

###################################################
### code chunk number 1: setup
###################################################
library(survival)


###################################################
### code chunk number 2: test
###################################################
tfun <- function(start, gap, birth= as.Date("1960-01-01")) {
    as.numeric(start-birth)/365.25 - as.numeric((start + gap)-birth)/365.25
}

test <- logical(200)
for (i in 1:200) {
    test[i] <- tfun(as.Date("2010/01/01"), 29) ==
               tfun(as.Date("2010/01/01") + i, 29)
}
table(test)


###################################################
### code chunk number 3: survrepeat
###################################################
getOption("SweaveHooks")[["fig"]]()
fit1a <- survfit(Surv(entry, futime, death) ~ 1, myeloma)
fit1b <- survfit(Surv(entry, futime, death) ~ 1, myeloma, id=id, robust=TRUE)
matplot(fit1a$time/365.25, cbind(fit1a$std.err, fit1b$std.err/fit1b$surv), 
        type='s',lwd=2, lty=1, col=2:3, #ylim=c(0, .6),
        xlab="Years post diagnosis", ylab="Estimated sd of log(surv)")
#
# when two valve seats failed at the same inspection, we need to jitter one
#  of the times, to avoid a (time1, time2) interval of length 0
ties <- which(with(valveSeat, diff(id)==0 & diff(time)==0))  #first of a tie
temp <- valveSeat$time
temp[ties] <- temp[ties] - .1
vdata <- valveSeat
vdata$time1 <- ifelse(!duplicated(vdata$id), 0, c(0, temp[-length(temp)]))
vdata$time2 <- temp
fit2a <- survfit(Surv(time1, time2, status) ~1, vdata)
fit2b <- survfit(Surv(time1, time2, status) ~1, vdata, id=id)
plot(fit2a, cumhaz=TRUE, xscale=365.25, xlab="Years in service",
     ylab="Estimated number of repairs")
lines(fit2b, cumhaz=TRUE, lty=c(1,3,3))
legend(150, 1.5, c("Estimate", "asymptotic se", "robust se"), lty=1:3, bty='n')

#
# PBC data, categorized by most recent bilirubin
# as an example of the EKM
pdata <- tmerge(subset(pbcseq, !duplicated(id), c(id, trt, age, sex, stage)),
                subset(pbcseq, !duplicated(id, fromLast=TRUE)), id,
                death= event(futime, status==2))
bcut <- cut(pbcseq$bili, c(0, 1.1, 5, 100), c('normal', 'moderate', 'high'))
pdata <- tmerge(pdata, pbcseq, id, cbili = tdc(day, bcut))
pdata$ibili <- pdata$cbili[match(pdata$id, pdata$id)] # initial bilirubin

ekm <- survfit(Surv(tstart, tstop, death) ~ cbili, pdata, id=id)
km  <- survfit(Surv(tstart, tstop, death) ~ ibili, pdata, id=id)
plot(ekm, fun='event', xscale=365.25, lwd=2, col=1:3, conf.int=TRUE,
     lty=2, conf.time=c(4,8,12)*365.25,
     xlab="Years post enrollment", ylab="Death")
lines(km, fun='event', lwd=1, col=1:3, lty=1)
#      conf.time= c(4.1, 8.1, 12.1)*365.25)
text(c(4600, 4300, 2600), c(.23, .56, .78), c("Normal", "Moderate", "High"),
     col=1:3, adj=0)
legend("topleft", c("KM", "EKM"), lty=1:2, col=1, lwd=2, bty='n')


###################################################
### code chunk number 4: auc
###################################################
getOption("SweaveHooks")[["fig"]]()
test <- survfit(Surv(time, status) ~1, aml, subset=(x=="Maintained"))
ntime <- length(test$time)
oldpar <- par(mfrow=c(1,2), mar=c(5,5,1,1))
plot(test, conf.int=FALSE, xmax=60)
jj <- (test$n.event > 0)
segments(test$time[jj], test$surv[jj], test$time[jj], 0, lty=2)
segments(55, test$surv[9], 55, 0, lty=2)
points(c(0, test$time[jj]), c(1, test$surv[jj]))
segments(0,0,55,0)
segments(0,0,0,1)

plot(test, conf.int=FALSE, xmax=60)
segments(pmin(test$time,55), test$surv, 55, test$surv, lty=2)
segments(55,test$surv[ntime],55,1)
segments(test$time[1], 1, 55, 1, lty=2)
points(c(test$time[jj],100),  .5*(c(1, test$surv[jj]) + c(test$surv[jj], 0)))
par(oldpar)


###################################################
### code chunk number 5: entrydata
###################################################
getOption("SweaveHooks")[["fig"]]()
mtest <- data.frame(id= c(1, 1, 1,  2,  3,  4, 4, 4,  5, 5, 5, 5),
                    t1= c(0, 4, 9,  0,  2,  0, 2, 8,  1, 3, 6, 8),
                    t2= c(4, 9, 10, 5,  9,  2, 8, 9,  3, 6, 8, 11),
                    st= c(1, 2,  1, 2,  3,  1, 3, 0,  2,  0,2, 0))

mtest$state <- factor(mtest$st, 0:3, c("censor", "a", "b", "c"))

temp <- survcheck(Surv(t1, t2, state) ~1, mtest, id=id)
plot(c(0,11), c(1,5.1), type='n', xlab="Time", ylab= "Subject")
with(mtest, segments(t1+.1, id, t2, id, lwd=2, col=as.numeric(temp$istate)))
event <- subset(mtest, state!='censor')
text(event$t2, event$id+.2, as.character(event$state))


###################################################
### code chunk number 6: competecheck
###################################################
m2 <- mgus2
m2$etime <- with(m2, ifelse(pstat==0, futime, ptime))
m2$event <- with(m2, ifelse(pstat==0, 2*death, 1))
m2$event <- factor(m2$event, 0:2, c('censor', 'pcm', 'death'))
m2$id <-1:nrow(m2)

# 20 year reporting time (240 months)
mfit <- survfit(Surv(etime, event) ~1, m2, id=id)

y3 <- (mfit$time <= 360 & rowSums(mfit$n.event) >0)  # rows of mfit, of interest
etot <- sum(m2$n.event[y3,])
nrisk <- mean(mfit$n.risk[y3,1])


###################################################
### code chunk number 7: checknafld
###################################################
ndata <- tmerge(nafld1[,1:8], nafld1, id=id, death= event(futime, status))
ndata <- tmerge(ndata, subset(nafld3, event=="nafld"), id, 
                nafld= tdc(days))
ndata <- tmerge(ndata, subset(nafld3, event=="diabetes"), id = id,
                diabetes = tdc(days), e1= cumevent(days))
ndata <- tmerge(ndata, subset(nafld3, event=="htn"),  id = id,
                htn = tdc(days), e2 = cumevent(days))
ndata <- tmerge(ndata, subset(nafld3, event=="dyslipidemia"), id=id,
                lipid = tdc(days), e3= cumevent(days))
ndata <- tmerge(ndata, subset(nafld3, event %in% c("diabetes", "htn", 
                                                   "dyslipidemia")), 
                id=id, comorbid= cumevent(days))
ndata$cstate <- with(ndata, factor(diabetes + htn + lipid, 0:3, 
                                   c("0mc", "1mc", "2mc", "3mc")))
temp <- with(ndata, ifelse(death, 4, comorbid))
ndata$event <- factor(temp, 0:4, 
         c("censored", "1mc", "2mc", "3mc", "death"))
ndata$age1 <- ndata$age + ndata$tstart/365.25   # analysis on age scale
ndata$age2 <- ndata$age + ndata$tstop/365.25

ndata2 <- subset(ndata, age2 > 50 & age1 < 90)
nfit <- survfit(Surv(age1, age2, event) ~1, ndata, id=id, start.time=50,
                p0=c(1,0,0,0,0), istate=cstate, se.fit = FALSE)
netime <- (nfit$time <=90 & rowSums(nfit$n.event) > 0)
# the number at risk at any time is all those in the intial state of a transition
#  at that time
from <- as.numeric(sub("\\.[0-9]*", "", colnames(nfit$n.transition)))
fmat <- model.matrix(~ factor(from, 1:5) -1)
temp <- (nfit$n.transition %*% fmat) >0 # TRUE for any transition 'from' state
frisk <- (nfit$n.risk * ifelse(temp,1, 0))
nrisk <- rowSums(frisk[netime,])
maxrisk <- apply(frisk[netime,],2,max)


###################################################
### code chunk number 8: skiplist1
###################################################
getOption("SweaveHooks")[["fig"]]()
sort2 <- order(ndata$age2)
plot(1:200, rev(sort2)[1:200], xlab="Addition to the list",
     ylab="Row index of the addition")


###################################################
### code chunk number 9: skiplist2
###################################################
getOption("SweaveHooks")[["fig"]]()
# simulate a skiplist with period 3
set.seed(1953)
n <- 30
yval <- sort(sample(1:200, n))
phat <- c(2/3, 2/9, 2/27)
y.ht <- rep(c(1,1,2,1,1,1,1,3,1,1,2,1,1,1,2,1), length=n)
plot(yval, rep(1,n), ylim=c(1,3), xlab="Data", ylab="")
indx <- which(y.ht > 1)
segments(yval[indx], rep(1, length(indx)), yval[indx], y.ht[indx])

y1 <- yval[indx]
y2 <- yval[y.ht==3]
lines(c(0, y2[1], y2[1], y1[5], y1[5], max(yval[yval < 100])),
      c(3,3, 2,2,1,1), lwd=2, col=2, lty=3)