97 lines
3.4 KiB
Plaintext
97 lines
3.4 KiB
Plaintext
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R Under development (unstable) (2019-06-28 r76752) -- "Unsuffered Consequences"
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Copyright (C) 2019 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> library(survival)
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> # Make sure that the old-style and new-style calls both work
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>
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> # new style
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> vet2 <- survSplit(Surv(time, status) ~ ., data= veteran, cut=c(90, 180),
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+ episode= "tgroup", id="id")
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> vet2[1:7, c("id", "tstart", "time", "status", "tgroup", "age", "karno")]
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id tstart time status tgroup age karno
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1 1 0 72 1 1 69 60
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2 2 0 90 0 1 64 70
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3 2 90 180 0 2 64 70
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4 2 180 411 1 3 64 70
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5 3 0 90 0 1 38 60
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6 3 90 180 0 2 38 60
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7 3 180 228 1 3 38 60
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>
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> # old style
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> vet3 <- survSplit(veteran, end='time', event='status', cut=c(90,180),
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+ episode="tgroup", id="id")
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> all.equal(vet2, vet3)
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[1] TRUE
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>
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> all.equal(nrow(vet2), nrow(veteran) + sum(veteran$time >90) +
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+ sum(veteran$time > 180))
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[1] TRUE
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>
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>
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> # Do a parallel computation using survSplit, and pyears/tcut. We should get
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> # the same answer.
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> # Break subjects up by year of entry and current time on study. Most of
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> # the deaths are within 3 months
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> # pyears complains (justifiably) about the obs with 0 days of fu, so we add 1
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> #
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> data1 <- jasa
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> data1$ayear <- as.numeric(substring(as.character(jasa$accept.dt), 1,4))
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> temp <- round(c(0, .25, 1,2,5)*365.25) # years
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> ftime <- tcut(rep(0, nrow(jasa)), temp,
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+ labels=paste(c(0, .25, 1:2), c(.25, 1,2,5), sep='-'))
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> pfit <- pyears(Surv(futime +1,fustat) ~ ayear + ftime, data1,
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+ scale=1)
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>
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> data2 <- survSplit(Surv(futime+1, fustat) ~ ., cut=temp, data=data1,
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+ episode = "tgroup")
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>
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> tab1 <- with(data2, tapply(fustat, list(ayear, tgroup), sum))
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> tab1 <- ifelse(is.na(tab1), 0, tab1)
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>
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> all.equal(as.vector(tab1), as.vector(pfit$event)) # ignore dimnames
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[1] TRUE
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>
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> tab2 <- with(data2, tapply(tstop-tstart, list(ayear, tgroup), sum))
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> tab2 <- ifelse(is.na(tab2), 0, tab2)
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>
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> all.equal(as.vector(tab2), as.vector(pfit$pyears))
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[1] TRUE
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>
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> # double check that the "data" option gives the same values
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> pfit2 <- pyears(Surv(futime +1,fustat) ~ ayear + ftime, data1,
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+ scale=1, data.frame=TRUE)$data
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> all.equal(pfit2$pyears, pfit$pyears[pfit$pyears >0])
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[1] TRUE
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> all.equal(pfit2$event, pfit$event[pfit$pyears >0])
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[1] TRUE
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>
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> # and that the rows of data2 have the right labels
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> keep <- which(pfit$pyears >0) # these are not in the data
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> rname <- rownames(pfit$pyears)[row(pfit$pyears)[keep]]
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> all.equal(rname, as.character(pfit2$ayear))
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[1] TRUE
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>
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> cname <- colnames(pfit$pyears)[col(pfit$pyears)[keep]]
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> all.equal(cname, as.character(pfit2$ftime))
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[1] TRUE
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>
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>
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>
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> proc.time()
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user system elapsed
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0.764 0.032 0.805
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