52 lines
1.4 KiB
Plaintext
52 lines
1.4 KiB
Plaintext
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R Under development (unstable) (2019-04-05 r76323) -- "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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> #
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> # Test out the rescaling done for Surv objects
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> #
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> library(rpart)
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> require(survival)
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Loading required package: survival
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> set.seed(10)
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>
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> aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
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> tdata <- data.frame(time=c(1,4,3,2,5,7,8,9,4), status=c(0,1,1,0,0,1,1,0,1),
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+ x=1:9)
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> fit2 <- rpart.exp(Surv(tdata$time, tdata$status), NULL, wt=rep(1,9))
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>
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> #
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> # Here is what it should be, in order
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> # for the intervals (0,3], (3,4], (4,7], (7,9]
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> deaths <- c( 1, 2, 1, 1)
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> pyears <- c(24, 6, 10, 3)
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> rate <- deaths/pyears
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> cumhaz <- cumsum(c(0, rate*c(3,1,3,2)))
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>
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> aeq(fit2$y[,2], tdata$status)
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[1] TRUE
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> aeq(fit2$y[,1], approx(c(0,3,4,7,9), cumhaz, tdata$time)$y)
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[1] TRUE
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>
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>
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>
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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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0.690 0.044 0.730
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