| plot.msfit | R Documentation | 
Plot method for an object of class "msfit". It plots the estimated
cumulative transition intensities in the multi-state model.
## S3 method for class 'msfit'
plot(
  x,
  type = c("single", "separate"),
  cols,
  xlab = "Time",
  ylab = "Cumulative hazard",
  ylim,
  lwd,
  lty,
  legend,
  legend.pos = "right",
  bty = "n",
  use.ggplot = FALSE,
  xlim,
  scale_type = "fixed",
  conf.int = 0.95,
  conf.type = "none",
  ...
)
| x | Object of class  | 
| type | One of  | 
| cols | A vector specifying colors for the different transitions;
default is 1:K (K no of transitions), when type= | 
| xlab | A title for the x-axis; default is  | 
| ylab | A title for the y-axis; default is  | 
| 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  | 
| lty | The line type, see  | 
| legend | Character vector of length equal to the number of transitions,
to be used in a legend; if missing, these will be taken from the row- and
column-names of the transition matrix contained in  | 
| legend.pos | The position of the legend, see  | 
| bty | The box type of the legend, see  | 
| use.ggplot | Default FALSE, set TRUE for ggplot version of plot | 
| xlim | Limits of x axis, relevant if use_ggplot = T | 
| scale_type | "fixed", "free", "free_x" or "free_y", see scales argument of facet_wrap(). Only relevant for use_ggplot = T. | 
| conf.int | Confidence level (%) from 0-1 for the cumulative hazard, default is 0.95. Only relevant for use_ggplot = T | 
| conf.type | Type of confidence interval - either "log" or "plain" . See
function details of  | 
| ... | Further arguments to plot | 
No return value
Hein Putter H.Putter@lumc.nl
Edouard F. Bonneville e.f.bonneville@lumc.nl
msfit
# 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)
# standard plot
plot(msf)
# specifying line width, color, and legend
plot(msf,lwd=2,col=c("darkgreen","darkblue","darkred"),legend=c("1->2","1->3","2->3"))
# separate plots
par(mfrow=c(2,2))
plot(msf,type="separate",lwd=2)
par(mfrow=c(1,1))
# ggplot version - see vignette for details
library(ggplot2)
plot(msf, use.ggplot = TRUE)
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