179 lines
6.2 KiB
Plaintext
179 lines
6.2 KiB
Plaintext
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R Under development (unstable) (2024-04-17 r86441) -- "Unsuffered Consequences"
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Copyright (C) 2024 The R Foundation for Statistical Computing
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Platform: aarch64-unknown-linux-gnu
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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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> options(na.action=na.exclude)
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> aeq <- function(x,y,...) all.equal(as.vector(x), as.vector(y), ...)
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>
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> # Make sure strata is retained, and that the overall variance is correct
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> fit1 <- coxph(Surv(time, status) ~ age + offset(ph.ecog*0) +strata(sex), lung)
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> fit2 <- coxph(Surv(time, status) ~ age + ph.ecog +strata(sex), lung)
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>
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> test <- concordance(fit1, fit2, influence=1)
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>
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> ksex <- model.frame(fit1)[["strata(sex)"]]
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> test1 <- concordance(fit1$y ~ fit1$linear.predictors + strata(ksex),
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+ reverse=TRUE, influence=1)
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> test2 <- concordance(fit1$y ~ fit2$linear.predictors + strata(ksex),
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+ reverse=TRUE, influence=1)
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> aeq(test$concordance, c(test1$concordance, test2$concordance))
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[1] TRUE
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> aeq(diag(test$var), c(test1$var[1], test2$var[1]))
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[1] TRUE
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> aeq(test$dfbeta, cbind(test1$dfbeta, test2$dfbeta))
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[1] TRUE
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>
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> cvec <- c(-1, 1)
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> aeq(cvec %*% test$var %*% cvec, sum((test1$dfbeta - test2$dfbeta)^2))
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[1] TRUE
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>
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> # Time weights
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> # Start with a very small data set: aml has 23 subjects
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> #
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> atest1 <- concordance(Surv(time, status) ~ x, aml, ranks=TRUE)
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> atest2 <- concordance(Surv(time, status) ~ x, aml, ranks=TRUE, timewt='S')
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> atest3 <- concordance(Surv(time, status) ~ x, aml, ranks=TRUE, timewt='S/G')
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> atest4 <- concordance(Surv(time, status) ~ x, aml, ranks=TRUE, timewt='n/G2')
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> # The ranks data frame agrees for all but weights
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> all.equal(atest1$ranks[, -3], atest2$ranks[, -3])
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[1] TRUE
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> all.equal(atest1$ranks[, -3], atest3$ranks[, -3])
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[1] TRUE
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> all.equal(atest1$ranks[, -3], atest4$ranks[, -3])
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[1] TRUE
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>
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> wt1 <- cbind(atest1$ranks[,"timewt"], atest2$ranks[,"timewt"],
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+ atest3$ranks[,"timewt"], atest4$ranks[,"timewt"])
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>
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> # survfit0 adds time 0 to the curves
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> # to break ties between censor/death for G, we need to add an offset to
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> # the censoring times. Since time is integer, .1 works nicely
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> s1 <- survfit0(survfit(Surv(time, status) ~ 1, aml))
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> g1 <- survfit0(survfit(Surv(time + .1*(1-status), 1-status) ~1, aml))
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>
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> # The ingredients of the weights
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> indx <- match(atest1$ranks[,"time"], s1$time)
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> nrisk <- s1$n.risk[indx]
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> sminus <- s1$surv[indx-1]
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> gminus <- g1$surv[findInterval(atest1$ranks[,"time"], g1$time)]
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> n <- nrow(aml)
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>
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> wt2 <- cbind(nrisk, n*sminus, n*sminus/gminus, nrisk/gminus^2)
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> aeq(wt1, wt2)
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[1] TRUE
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>
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> # The sum of weighted ranks should equal (C-D) for a Cox model fit
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> tfun <- function(cfit, reverse=FALSE) {
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+ t1 <- sum(cfit$ranks$timewt * cfit$ranks$rank)
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+ t2 <- cfit$count[1] - cfit$count[2]
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+ all.equal(unname(t1), unname(t2))
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+ }
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> tfun(atest1)
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[1] TRUE
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> tfun(atest2)
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[1] TRUE
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> tfun(atest3)
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[1] TRUE
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> tfun(atest4)
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[1] TRUE
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>
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> # The nafld data set has strong and early censoring (one of the only ones
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> # in the package that does.) So it is a good check of time weights.
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> #
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> nfit <- coxph(Surv(futime, status) ~ male + pspline(age), nafld1)
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> cn1 <- concordance(nfit, timewt='n', ranks=TRUE)
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> cn2 <- concordance(nfit, timewt='S', ranks=TRUE)
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> cn3 <- concordance(nfit, timewt='S/G', ranks=TRUE)
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> cn4 <- concordance(nfit, timewt='n/G2', ranks=TRUE)
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>
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> sfit <- survfit0(survfit(Surv(futime, status) ~ 1, nafld1))
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> gfit <- survfit0(survfit(Surv(futime + .1*(status==0), 1-status) ~0, nafld1))
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>
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> # The ingredients of the weights
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> dtime <- cn1$ranks[, "time"]
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> indx <- match(dtime, sfit$time)
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> nrisk <- sfit$n.risk[indx]
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> sminus <- sfit$surv[indx-1]
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> gminus <- gfit$surv[findInterval(dtime, gfit$time)]
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> n <- nrow(nafld1)
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>
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> wt1 <- cbind(cn1$ranks[, "timewt"], cn2$ranks[,"timewt"],
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+ cn3$ranks[, "timewt"], cn4$ranks[,"timewt"])
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> wt2 <- cbind(nrisk, n*sminus, n*sminus/gminus, nrisk/gminus^2)
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> aeq(wt1, wt2)
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[1] TRUE
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>
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> rd1 <- cn1$ranks
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> rd2 <- cn2$ranks
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> rd3 <- cn3$ranks
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> all.equal(rd1[c('time', 'rank', 'casewt')], rd2[c('time', 'rank', 'casewt')])
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[1] TRUE
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> all.equal(rd1[c('time', 'rank', 'casewt')], rd3[c('time', 'rank', 'casewt')])
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[1] TRUE
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>
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> tfun(cn1)
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[1] TRUE
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> tfun(cn2)
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[1] TRUE
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> tfun(cn3)
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[1] TRUE
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> tfun(cn4)
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[1] TRUE
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>
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> # Simple check of (time1, time2) data
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> # First a check on the fastkm2 (internal) routine
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> test1 <- survfit(Surv(tstart, tstop, status) ~1, cgd, id=id)
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> nr <- nrow(cgd)
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> y <- with(cgd, Surv(tstart,tstop, status))
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> sort1 <- order(-cgd$tstart); sort2 <- order(-cgd$tstop, cgd$status)
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> if (!exists("Cfastkm2")) Cfastkm2 <- survival:::Cfastkm2 # for my test env
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> test2 <- .Call(Cfastkm2, y, rep(1.0, nr), order(-cgd$tstart)-1L,
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+ order(-cgd$tstop, cgd$status) -1L)
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> ii <- which(test1$n.event>0)
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> all.equal(test1$time[ii], test2$etime)
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[1] TRUE
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> all.equal(test1$n.risk[ii], test2$nrisk)
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[1] TRUE
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> all.equal(c(1, test1$surv[ii[-length(ii)]]), test2$S) # test 2 is lagged
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[1] TRUE
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>
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> zero <- rep(0, nrow(nafld1))
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> test3 <- survfit(Surv(futime, status) ~1, nafld1, id=id)
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> test4 <- with(nafld1, .Call(Cfastkm2, Surv(zero, futime, status), zero+1,
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+ seq.int(nrow(nafld1)) -1L,
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+ order(-futime, status) -1L))
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> ii <- which(test3$n.event >0)
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> all.equal(test3$time[ii], test4$etime)
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[1] TRUE
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> all.equal(test3$n.risk[ii], test4$nrisk)
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[1] TRUE
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> all.equal(c(1, test3$surv[ii[-length(ii)]]), test4$S) # test 2 is lagged
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[1] TRUE
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>
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> # Now a check of concordance
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> nfitx <- coxph(Surv(zero, futime, status) ~ male + pspline(age), nafld1)
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> cn1x <- concordance(nfitx, timewt='n', ranks=TRUE)
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> cn2x <- concordance(nfitx, timewt='S', ranks=TRUE)
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> all.equal(cn1x$count, cn1$count)
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[1] TRUE
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> all.equal(cn2x$count, cn2$count)
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[1] TRUE
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>
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> proc.time()
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user system elapsed
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0.710 0.023 0.731
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