mstate (version 0.2.6)

plot.probtrans: Plot method for a probtrans object

Description

Plot method for an object of class 'probtrans'. It plots the transition probabilities as estimated by probtrans.

Usage

## S3 method for class 'probtrans':
plot(x,from=1,
    type=c("stacked","filled","single","separate"),ord,
    cols,xlab="Time",ylab="Probability",xlim,ylim,lwd,lty,cex,
    legend,legend.pos,bty="o",...)

Arguments

x

Object of class 'probtrans', containing estimated transition probabilities

from

The starting state from which the probabilities are used to plot

type

One of "stacked" (default), "filled", "single" or "separate"; in case of "stacked", the transition probabilities are stacked and the distance between two adjacent curves indicates t

ord

A vector of length equal to the number of states, specifying the order of plotting in case type="stacked" or "filled"

cols

A vector specifying colors for the different transitions; default is 1:K (K no of transitions), when type="single", and 1 (black), when type="separate"

xlab

A title for the x-axis; default is "Time"

ylab

A title for the y-axis; default is "Probability"

xlim

The x limits of the plot(s), default is range of time

ylim

The y limits of the plot(s); if ylim is specified for type="separate", then all plots use the same ylim for y limits

lwd

The line width, see par; default is 1

lty

The line type, see par; default is 1

cex

Character size, used in text; only used when type="stacked" or "filled"

legend

Character vector of length equal to the number of transitions, to be used in a legend; if missing, numbers will be used; this and the legend arguments following are ignored when type="separate"

legend.pos

The position of the legend, see legend; default is "topleft"

bty

The box type of the legend, see legend

...

Further arguments to plot

Value

• No return value

See Also

probtrans

Examples

# transition matrix for illness-death model
tmat <- trans.illdeath()
# data in wide format, for transition 1 this is dataset E1 of
# Therneau & 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 & Grambsch (2000)
newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
msf <- msfit(cx,newdata,trans=tmat)
# probtrans
pt <- probtrans(msf,predt=0)
# default plot
plot(pt,ord=c(2,3,1),lwd=2,cex=0.75)
# filled plot
plot(pt,type="filled",ord=c(2,3,1),lwd=2,cex=0.75)
# single plot
plot(pt,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,type="sep",lwd=2)
par(mfrow=c(1,1))