227 lines
7.4 KiB
Plaintext
227 lines
7.4 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) # preserve missings
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> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
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>
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> #
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> # Tests from the appendix of Therneau and Grambsch
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> # b. Data set 1 and Efron estimate
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> #
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> test1 <- data.frame(time= c(9, 3,1,1,6,6,8),
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+ status=c(1,NA,1,0,1,1,0),
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+ x= c(0, 2,1,1,1,0,0))
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>
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> byhand <- function(beta, newx=0) {
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+ r <- exp(beta)
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+ loglik <- 2*beta - (log(3*r +3) + log((r+5)/2) + log(r+3))
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+ u <- (30 + 23*r - r^3)/ ((r+1)*(r+3)*(r+5))
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+ tfun <- function(x) x - x^2
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+ imat <- tfun(r/(r+1)) + tfun(r/(r+5)) + tfun(r/(r+3))
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+
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+ # The matrix of weights, one row per obs, one col per time
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+ # Time of 1, 6, 6+0 (second death), and 9
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+ wtmat <- matrix(c(1,1,1,1,1,1,
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+ 0,0,1,1,1,1,
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+ 0,0,.5, .5, 1,1,
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+ 0,0,0,0,0,1), ncol=4)
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+ wtmat <- diag(c(r,r,r,1,1,1)) %*% wtmat
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+
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+ x <- c(1,1,1,0,0,0)
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+ status <- c(1,0,1,1,0,1)
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+ xbar <- colSums(wtmat*x)/ colSums(wtmat)
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+ haz <- 1/ colSums(wtmat) # one death at each of the times
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+
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+ hazmat <- wtmat %*% diag(haz) #each subject's hazard over time
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+ mart <- status - rowSums(hazmat)
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+
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+ a <- r+1; b<- r+3; d<- r+5 # 'c' in the book, 'd' here
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+ score <- c((2*r + 3)/ (3*a^2),
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+ -r/ (3*a^2),
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+ (675+ r*(1305 +r*(756 + r*(-4 +r*(-79 -13*r)))))/(3*(a*b*d)^2),
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+ r*(1/(3*a^2) - a/(2*b^2) - b/(2*d^2)),
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+ 2*r*(177 + r*(282 +r*(182 + r*(50 + 5*r)))) /(3*(a*b*d)^2),
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+ 2*r*(177 + r*(282 +r*(182 + r*(50 + 5*r)))) /(3*(a*b*d)^2))
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+
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+ # Schoenfeld residual
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+ d <- mean(xbar[2:3])
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+ scho <- c(1/(r+1), 1- d, 0- d , 0)
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+
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+ surv <- exp(-cumsum(haz)* exp(beta*newx))[c(1,3,4)]
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+ varhaz.g <- cumsum(haz^2) # since all numerators are 1
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+
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+ varhaz.d <- cumsum((newx-xbar) * haz)
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+
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+ varhaz <- (varhaz.g + varhaz.d^2/ imat) * exp(2*beta*newx)
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+
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+ list(loglik=loglik, u=u, imat=imat, xbar=xbar, haz=haz,
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+ mart=mart, score=score, var.g=varhaz.g, var.d=varhaz.d,
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+ scho=scho, surv=surv, var=varhaz[c(1,3,4)])
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+ }
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>
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>
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> aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
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>
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> fit0 <-coxph(Surv(time, status) ~x, test1, iter=0)
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> truth0 <- byhand(0,0)
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> aeq(truth0$loglik, fit0$loglik[1])
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[1] TRUE
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> aeq(1/truth0$imat, fit0$var)
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[1] TRUE
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> aeq(truth0$mart, fit0$residuals[c(2:6,1)])
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[1] TRUE
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> aeq(resid(fit0), c(-3/4, NA, 5/6, -1/6, 5/12, 5/12, -3/4))
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[1] TRUE
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> aeq(truth0$scho, resid(fit0, 'schoen'))
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[1] TRUE
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> aeq(truth0$score, resid(fit0, 'score')[c(3:7,1)])
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[1] TRUE
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> sfit <- survfit(fit0, list(x=0), censor=FALSE)
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> aeq(sfit$std.err^2, truth0$var)
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[1] TRUE
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> aeq(sfit$surv, truth0$surv)
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[1] TRUE
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>
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> fit <- coxph(Surv(time, status) ~x, test1, eps=1e-8, nocenter=NULL)
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> aeq(round(fit$coefficients,6), 1.676857)
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[1] TRUE
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> truebeta <- log(cos(acos((45/23)*sqrt(3/23))/3) * 2* sqrt(23/3))
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> truth <- byhand(truebeta, 0)
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> aeq(truth$loglik, fit$loglik[2])
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[1] TRUE
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> aeq(1/truth$imat, fit$var)
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[1] TRUE
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> aeq(truth$mart, fit$residuals[c(2:6,1)])
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[1] TRUE
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> aeq(truth$scho, resid(fit, 'schoen'))
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[1] TRUE
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> aeq(truth$score, resid(fit, 'score')[c(3:7,1)])
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[1] TRUE
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>
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> # Per comments in the source code, the below is expected to fail for Efron
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> # at the tied death times. (When predicting for new data, predict
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> # treats a time in the new data set that exactly matches one in the original
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> # as being just after the original, i.e., experiences the full hazard
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> # jump there, in the same way that censors do.)
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> expect <- predict(fit, type='expected', newdata=test1) #force recalc
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> use <- !(test1$time==6 | is.na(test1$status))
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> aeq(test1$status[use] - resid(fit)[use], expect[use])
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[1] TRUE
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>
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> sfit <- survfit(fit, list(x=0), censor=FALSE)
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> aeq(sfit$surv, truth$surv)
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[1] TRUE
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> aeq(sfit$std.err^2, truth$var)
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[1] TRUE
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>
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> #
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> # Done with the formal test, now print out lots of bits
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> #
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> resid(fit)
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1 2 3 4 5 6 7
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-0.3655434 NA 0.7191707 -0.2808293 -0.4383414 0.7310869 -0.3655434
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> resid(fit, 'scor')
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1 2 3 4 5 6 7
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0.2208584 NA 0.1132780 -0.0442340 -0.1029199 -0.4078409 0.2208584
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> resid(fit, 'scho')
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1 6 6 9
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0.157512 0.421244 -0.578756 0.000000
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>
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> predict(fit, type='lp')
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[1] -0.8384287 NA 0.8384287 0.8384287 0.8384287 -0.8384287 -0.8384287
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> predict(fit, type='risk')
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[1] 0.4323894 NA 2.3127302 2.3127302 2.3127302 0.4323894 0.4323894
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> predict(fit, type='expected')
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1 2 3 4 5 6 7
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1.3655434 NA 0.2808293 0.2808293 1.4383414 0.2689131 0.3655434
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> predict(fit, type='terms')
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x
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1 -0.8384287
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2 NA
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3 0.8384287
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4 0.8384287
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5 0.8384287
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6 -0.8384287
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7 -0.8384287
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> predict(fit, type='lp', se.fit=T)
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$fit
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1 2 3 4 5 6 7
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-0.8384287 NA 0.8384287 0.8384287 0.8384287 -0.8384287 -0.8384287
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$se.fit
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1 2 3 4 5 6 7
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0.6388078 NA 0.6388078 0.6388078 0.6388078 0.6388078 0.6388078
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> predict(fit, type='risk', se.fit=T)
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$fit
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1 2 3 4 5 6 7
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0.4323894 NA 2.3127302 2.3127302 2.3127302 0.4323894 0.4323894
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$se.fit
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1 2 3 4 5 6 7
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0.4200565 NA 0.9714774 0.9714774 0.9714774 0.4200565 0.4200565
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> predict(fit, type='expected', se.fit=T)
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$fit
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1 2 3 4 5 6 7
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1.3655434 NA 0.2808293 0.2808293 1.4383414 0.2689131 0.3655434
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$se.fit
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[1] 1.0649293 NA 0.2864593 0.2864593 1.5922983 0.3661617 0.3661617
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> predict(fit, type='terms', se.fit=T)
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$fit
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x
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1 -0.8384287
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2 NA
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3 0.8384287
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4 0.8384287
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5 0.8384287
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6 -0.8384287
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7 -0.8384287
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$se.fit
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x
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1 0.6388078
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2 NA
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3 0.6388078
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4 0.6388078
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5 0.6388078
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6 0.6388078
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7 0.6388078
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>
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> summary(survfit(fit))
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Call: survfit(formula = fit)
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time n.risk n.event survival std.err lower 95% CI upper 95% CI
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1 6 1 0.8857 0.117 0.683036 1
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6 4 2 0.4294 0.237 0.145743 1
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9 1 1 0.0425 0.116 0.000198 1
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> summary(survfit(fit, list(x=2)))
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Call: survfit(formula = fit, newdata = list(x = 2))
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time n.risk n.event survival std.err lower 95% CI upper 95% CI
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1 6 1 2.23e-01 5.97e-01 1.16e-03 1
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6 4 2 2.87e-05 5.69e-04 3.96e-22 1
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9 1 1 1.08e-17 1.04e-15 1.07e-99 1
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>
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> proc.time()
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user system elapsed
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0.429 0.028 0.454
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