Plot method for a probtrans object

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

# S3 method for probtrans
plot(
  x,
  from = 1,
  type = c("filled", "single", "separate", "stacked"),
  ord,
  cols,
  xlab = "Time",
  ylab = "Probability",
  xlim,
  ylim,
  lwd,
  lty,
  cex,
  legend,
  legend.pos = "right",
  bty = "n",
  xaxs = "i",
  yaxs = "i",
  use.ggplot = FALSE,
  conf.int = 0.95,
  conf.type = c("log", "plain", "none"),
  label,
  ...
)

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 the probability, this is also true for `"filled"`, but the space between adjacent curves are filled, in case of `"single"`, the probabilities are shown as different curves in a single plot, in case of `"separate"`, separate plots are shown for the estimated transition probabilities
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 a palette from green to red, when type=`"filled"` (reordered according to `ord`, and 1 (black), otherwise
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`
xaxs	See `par`, default is "i", for type=`"stacked"`
yaxs	See `par`, default is "i", for type=`"stacked"`
use.ggplot	Default FALSE, set TRUE for ggplot version of plot
conf.int	Confidence level (%) from 0-1 for probabilities, default is 0.95 (95% CI). Setting to 0 removes the CIs.
conf.type	Type of confidence interval - either "log" or "plain" . See function details for details.
label	Only relevant for type = "filled" or "stacked", set to "annotate" to have state labels on plot, or leave unspecified.
...	Further arguments to plot

Value

No return value

Details

Regarding confidence intervals: let $p$ denote a predicted probability, $\sigma$ its estimated standard error, and $z_{\alpha/2}$ denote the critical value of the standard normal distribution at confidence level $1 - \alpha$.

The confidence interval of type "plain" is then $$p \pm z_{\alpha/2} * \sigma$$

The confidence interval of type "log", based on the Delta method, is then $$\exp(\log(p) \pm z_{\alpha/2} * \sigma / p)$$

Author

Hein Putter H.Putter@lumc.nl

Edouard F. Bonneville e.f.bonneville@lumc.nl

Examples


# 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)
#> $Frequencies
#>          to
#> from      healthy illness death no event total entering
#>   healthy       0       4     2        0              6
#>   illness       0       0     4        0              4
#>   death         0       0     0        6              6
#> 
#> $Proportions
#>          to
#> from        healthy   illness     death  no event
#>   healthy 0.0000000 0.6666667 0.3333333 0.0000000
#>   illness 0.0000000 0.0000000 1.0000000 0.0000000
#>   death   0.0000000 0.0000000 0.0000000 1.0000000
#> 
table(tglong$status,tglong$to,tglong$from)
#> , ,  = 1
#> 
#>    
#>     2 3
#>   0 2 4
#>   1 4 2
#> 
#> , ,  = 2
#> 
#>    
#>     2 3
#>   0 0 0
#>   1 0 4
#> 
# 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)
#> Call:
#> coxph(formula = Surv(Tstart, Tstop, status) ~ x1.1 + x2.2 + strata(trans), 
#>     data = tglong, method = "breslow")
#> 
#>   n= 16, number of events= 10 
#> 
#>        coef exp(coef) se(coef)     z Pr(>|z|)
#> x1.1 1.4753    4.3723   1.2557 1.175    0.240
#> x2.2 0.8571    2.3563   0.8848 0.969    0.333
#> 
#>      exp(coef) exp(-coef) lower .95 upper .95
#> x1.1     4.372     0.2287    0.3731     51.24
#> x2.2     2.356     0.4244    0.4160     13.35
#> 
#> Concordance= 0.781  (se = 0.077 )
#> Likelihood ratio test= 2.93  on 2 df,   p=0.2
#> Wald test            = 2.32  on 2 df,   p=0.3
#> Score (logrank) test = 2.86  on 2 df,   p=0.2
#> 
# 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(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))

# ggplot version - see vignette for details
library(ggplot2)
plot(pt, ord=c(2,3,1), use.ggplot = TRUE)

Arguments

Value

Details

See also

Author

Examples