Nothing
test_that("estimate_support_msm", {
skip_if(TRUE)
# Start with a difficult one right away
set.seed(3)
#Absorbing states 4 and 6
qmatrix <- rbind(
c(-0.6, 0.1, 0.5, 0, 0, 0),
c(0.08, -0.205, 0, 0, 0.125, 0),
c(0, 0, -0.25, 0.25, 0, 0),
c(0, 0, 0, 0, 0, 0),
c(0, 0, 0, 0, -0.4, 0.4),
c(0, 0, 0, 0, 0, 0)
)
n <- 100
gd <- NULL
for (i in 1:n) {
smsm <- msm::sim.msm(qmatrix, 14)
# inspection times uniformly on 2 year intervals until 14 years
itimes <- seq(0, 12, by=2) + runif(7, 0, 2)
gdi <- data.frame(id=i, state=evalstep(time=smsm$times, stepf=smsm$states,
newtime=c(0,itimes)), time=c(0,itimes))
# throw away superfluous duplicate 4's ad 6's (absorbing)
gdi <- gdi[!(gdi$state==4 & duplicated(gdi$state)),]
gdi <- gdi[!(gdi$state==6 & duplicated(gdi$state)),]
gd <- rbind(gd, gdi)
}
head(gd, n=12)
tail(gd, n=12)
tmat <- mstate::transMat(x = list( c(2, 3), c(1, 5), c(4), c(), c(6), c() ))
tmat2 <- mstate::to.trans2(tmat)
})
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