View source: R/plot.probtrans.subjects.R
plot.probtrans.subjects | R Documentation |
"probtrans.subjects"
Plots the transition probabilities for a specific subject. Wrapper for
plot.probtrans
## S3 method for class 'probtrans.subjects'
plot(x, id, ...)
x |
An object of class |
id |
Subject identifier |
... |
Further arguments to |
Note that
Hein Putter and Daniel Gomon
# transition matrix for illness-death model
tmat <- trans.illdeath()
# data in wide format, for transition 1 this is dataset E1 of
# Therneau and Grambsch (2000)
tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1),
x1=c(1,1,1,0,0,0),x2=c(6:1))
# data in long format using msprep
tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
data=tg,keep=c("x1","x2"),trans=tmat)
# events
events(tglong)
table(tglong$status,tglong$to,tglong$from)
# expanded covariates
tglong <- expand.covs(tglong,c("x1","x2"))
# Cox model with different covariate
cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
data=tglong,method="breslow")
summary(cx)
# new data, to check whether results are the same for transition 1 as
# those in appendix E.1 of Therneau and Grambsch (2000)
newdata <- data.frame(id=rep(1:3, each = 3),x1.1=c(0,0,0,1,0,1,0,1,0),
x2.2=c(0,1,0,0,0,0,1,0,1), trans = rep(1:3, 3))
msf <- msfit_subjects(cx,newdata,trans=tmat)
# probtrans
pt <- probtrans_subjects(msf,predt=0)
# default plot
plot(pt,id=2,ord=c(2,3,1),lwd=2,cex=0.75)
# filled plot
plot(pt,id=3,type="filled",ord=c(2,3,1),lwd=2,cex=0.75)
# single plot
plot(pt,id=1,type="single",lwd=2,col=rep(1,3),lty=1:3,legend.pos=c(8,1))
# separate plots
par(mfrow=c(2,2))
plot(pt,id=1,type="sep",lwd=2)
par(mfrow=c(1,1))
# ggplot version - see vignette for details
library(ggplot2)
plot(pt,id=1,ord=c(2,3,1), use.ggplot = TRUE)
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