R Under development (unstable) (2023-12-02 r85657 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # > # Test out the rescaling done for Surv objects > # > library(rpart) > require(survival) Loading required package: survival > set.seed(10) > > aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...) > 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), + x=1:9) > fit2 <- rpart.exp(Surv(tdata$time, tdata$status), NULL, wt=rep(1,9)) > > # > # Here is what it should be, in order > # for the intervals (0,3], (3,4], (4,7], (7,9] > deaths <- c( 1, 2, 1, 1) > pyears <- c(24, 6, 10, 3) > rate <- deaths/pyears > cumhaz <- cumsum(c(0, rate*c(3,1,3,2))) > > aeq(fit2$y[,2], tdata$status) [1] TRUE > aeq(fit2$y[,1], approx(c(0,3,4,7,9), cumhaz, tdata$time)$y) [1] TRUE > > > > > > > proc.time() user system elapsed 0.85 0.12 0.92