# Plotting event probabilities over time Function to plot survival probabilities or absolute risks (cumulative incidence function) against time. ```r ## S3 method for class 'prodlim' plot( x, type, cause, select, newdata, add = FALSE, col, lty, lwd, ylim, xlim, ylab, xlab = "Time", num.digits = 2, timeconverter, legend = TRUE, short.labels = TRUE, logrank = FALSE, marktime = FALSE, confint = TRUE, automar, atrisk = ifelse(add, FALSE, TRUE), timeOrigin = 0, axes = TRUE, background = TRUE, percent = TRUE, minAtrisk = 0, limit = 10, ... ) ``` ## Arguments - `x`: an object of class `prodlim' as returned by the `prodlim` function. - `type`: Either `"surv"` or `"risk"` AKA `"cuminc"`. Controls what part of the object is plotted. Defaults to `object$type`. - `cause`: For competing risk models. Character (other classes are converted with `as.character`). The argument `cause` determines the event of interest. Currently one cause is allowed at a time, but you can call the function again with `add=TRUE` to add the lines of the other causes. Also, if `cause="stacked"` is specified the absolute risks of all causes are stacked. - `select`: Select which lines to plot. This can be used when there are many strata or many competing risks to select a subset of the lines. However, a more clean way to select covariate strata is to use the argument `newdata`. Another application is when there are several competing risks and the stacked plot (`cause="stacked"`) should only show a selected subset of the available causes. - `newdata`: a data frame containing covariate strata for which to show curves. When omitted element `X` of object `x` is used. - `add`: if `TRUE` curves are added to an existing plot. - `col`: color for curves. Default is `1:number(curves)` - `lty`: line type for curves. Default is 1. - `lwd`: line width for all curves. Default is 3. - `ylim`: limits of the y-axis - `xlim`: limits of the x-axis - `ylab`: label for the y-axis - `xlab`: label for the x-axis - `num.digits`: Number of digits when rounding off numerical values for legend and at-risk tables. - `timeconverter`: The following options are supported: "days2years" (conversion factor: 1/365.25) "months2years" (conversion factor: 1/12) "days2months" (conversion factor 1/30.4368499) "years2days" (conversion factor 365.25) "years2months" (conversion factor 12) "months2days" (conversion factor 30.4368499) - `legend`: if TRUE a legend is plotted by calling the function legend. Optional arguments of the function `legend` can be given in the form `legend.x=val` where x is the name of the argument and val the desired value. See also Details. - `short.labels`: Logical. When `FALSE` construct labels as cause=1, var1=v1, var2=v2 else as 1, v1, v2. - `logrank`: If TRUE, the logrank p-value will be extracted from a call to `survdiff` and added to the legend. This works only for survival models, i.e. Kaplan-Meier with discrete predictors. - `marktime`: if TRUE the curves are tick-marked at right censoring times by invoking the function `markTime`. Optional arguments of the function `markTime` can be given in the form `confint.x=val` as with legend. See also Details. - `confint`: if TRUE pointwise confidence intervals are plotted by invoking the function `confInt`. Optional arguments of the function `confInt` can be given in the form `confint.x=val` as with legend. See also Details. - `automar`: If TRUE the function trys to find suitable values for the figure margins around the main plotting region. - `atrisk`: if TRUE display numbers of subjects at risk by invoking the function `atRisk`. Optional arguments of the function `atRisk` can be given in the form `atrisk.x=val` as with legend. See also Details. - `timeOrigin`: Start of the time axis - `axes`: If true axes are drawn. See details. - `background`: If `TRUE` the background color and grid color can be controlled using smart arguments SmartControl, such as background.bg="yellow" or background.bg=c("gray66","gray88"). The following defaults are passed to `background` by `plot.prodlim`: horizontal=seq(0,1,.25), vertical=NULL, bg="gray77", fg="white". See `background` for all arguments, and the examples below. - `percent`: If true the y-axis is labeled in percent. - `minAtrisk`: Integer. Show the curve only until the number at-risk is at least `minAtrisk` - `limit`: When newdata is not specified and the number of lines in element `X` of object `x` exceeds limits, only the results for covariate constellations of the first, the middle and the last row in `X` are shown. Otherwise all lines of `X` are shown. - `...`: Parameters that are filtered by `SmartControl` and then passed to the functions `plot`, `legend`, `axis`, `atRisk`, `confInt`, `markTime`, `backGround` ## Returns The (invisible) object. ## Details From version 1.1.3 on the arguments legend.args, atrisk.args, confint.args are obsolete and only available for backward compatibility. Instead arguments for the invoked functions `atRisk`, `legend`, `confInt`, `markTime`, `axis` are simply specified as `atrisk.cex=2`. The specification is not case sensitive, thus `atRisk.cex=2` or `atRISK.cex=2` will have the same effect. The function `axis` is called twice, and arguments of the form `axis1.labels`, `axis1.at` are used for the time axis whereas `axis2.pos`, `axis1.labels`, etc. are used for the y-axis. These arguments are processed via `...{}` of `plot.prodlim` and inside by using the function `SmartControl`. Documentation of these arguments can be found in the help pages of the corresponding functions. ## Examples ```r ## simulate right censored data from a two state model set.seed(100) dat <- SimSurv(100) # with(dat,plot(Hist(time,status))) ### marginal Kaplan-Meier estimator kmfit <- prodlim(Hist(time, status) ~ 1, data = dat) plot(kmfit) plot(kmfit,atrisk.show.censored=1L,atrisk.at=seq(0,12,3)) plot(kmfit,timeconverter="years2months") # change time range plot(kmfit,xlim=c(0,4)) # change scale of y-axis plot(kmfit,percent=FALSE) # mortality instead of survival plot(kmfit,type="risk") # change axis label and position of ticks plot(kmfit, xlim=c(0,10), axis1.at=seq(0,10,1), axis1.labels=0:10, xlab="Years", axis2.las=2, atrisk.at=seq(0,10,2.5), atrisk.title="") # change background color plot(kmfit, xlim=c(0,10), confint.citype="shadow", col=1, axis1.at=0:10, axis1.labels=0:10, xlab="Years", axis2.las=2, atrisk.at=seq(0,10,2.5), atrisk.title="", background=TRUE, background.fg="white", background.horizontal=seq(0,1,.25/2), background.vertical=seq(0,10,2.5), background.bg=c("gray88")) # change type of confidence limits plot(kmfit, xlim=c(0,10), confint.citype="dots", col=4, background=TRUE, background.bg=c("white","gray88"), background.fg="gray77", background.horizontal=seq(0,1,.25/2), background.vertical=seq(0,10,2)) ### Kaplan-Meier in discrete strata kmfitX <- prodlim(Hist(time, status) ~ X1, data = dat) plot(kmfitX,atrisk.show.censored=1L) # move legend plot(kmfitX,legend.x="bottomleft",atRisk.cex=1.3, atrisk.title="No. subjects") ## Control the order of strata ## since version 1.5.1 prodlim does obey the order of ## factor levels dat$group <- factor(cut(dat$X2,c(-Inf,0,0.5,Inf)), labels=c("High","Intermediate","Low")) kmfitG <- prodlim(Hist(time, status) ~ group, data = dat) plot(kmfitG) ## relevel dat$group2 <- factor(cut(dat$X2,c(-Inf,0,0.5,Inf)), levels=c("(0.5, Inf]","(0,0.5]","(-Inf,0]"), labels=c("Low","Intermediate","High")) kmfitG2 <- prodlim(Hist(time, status) ~ group2, data = dat) plot(kmfitG2) # add log-rank test to legend plot(kmfitX, atRisk.cex=1.3, logrank=TRUE, legend.x="topright", atrisk.title="at-risk") # change atrisk labels plot(kmfitX, legend.x="bottomleft", atrisk.title="Patients", atrisk.cex=0.9, atrisk.labels=c("X1=0","X1=1")) # multiple categorical factors kmfitXG <- prodlim(Hist(time,status)~X1+group2,data=dat) plot(kmfitXG,select=1:2) ### Kaplan-Meier in continuous strata kmfitX2 <- prodlim(Hist(time, status) ~ X2, data = dat) plot(kmfitX2,xlim=c(0,10)) # specify values of X2 for which to show the curves plot(kmfitX2,xlim=c(0,10),newdata=data.frame(X2=c(-1.8,0,1.2))) ### Cluster-correlated data library(survival) cdat <- cbind(SimSurv(20),patnr=sample(1:5,size=20,replace=TRUE)) kmfitC <- prodlim(Hist(time, status) ~ cluster(patnr), data = cdat) plot(kmfitC) plot(kmfitC,atrisk.labels=c("Units","Patients")) kmfitC2 <- prodlim(Hist(time, status) ~ X1+cluster(patnr), data = cdat) plot(kmfitC2) plot(kmfitC2,atrisk.labels=c("Teeth","Patients","Teeth","Patients"), atrisk.col=c(1,1,2,2)) ### Cluster-correlated data with strata n = 50 foo = runif(n) bar = rexp(n) baz = rexp(n,1/2) d = stack(data.frame(foo,bar,baz)) d$cl = sample(10, 3*n, replace=TRUE) fit = prodlim(Surv(values) ~ ind + cluster(cl), data=d) plot(fit) ## simulate right censored data from a competing risk model datCR <- SimCompRisk(100) with(datCR,plot(Hist(time,event))) ### marginal Aalen-Johansen estimator ajfit <- prodlim(Hist(time, event) ~ 1, data = datCR) plot(ajfit) # same as plot(ajfit,cause=1) plot(ajfit,atrisk.show.censored=1L) # cause 2 plot(ajfit,cause=2) # both in one plot(ajfit,cause=1) plot(ajfit,cause=2,add=TRUE,col=2) ### stacked plot plot(ajfit,cause="stacked",select=2) ### stratified Aalen-Johansen estimator ajfitX1 <- prodlim(Hist(time, event) ~ X1, data = datCR) plot(ajfitX1) ## add total number at-risk to a stratified curve ttt = 1:10 plot(ajfitX1,atrisk.at=ttt,col=2:3) plot(ajfit,add=TRUE,col=1) atRisk(ajfit,newdata=datCR,col=1,times=ttt,line=3,labels="Total") ## stratified Aalen-Johansen estimator in nearest neighborhoods ## of a continuous variable ajfitX <- prodlim(Hist(time, event) ~ X1+X2, data = datCR) plot(ajfitX,newdata=data.frame(X1=c(1,1,0),X2=c(4,10,10))) plot(ajfitX,newdata=data.frame(X1=c(1,1,0),X2=c(4,10,10)),cause=2) ## stacked plot plot(ajfitX, newdata=data.frame(X1=0,X2=0.1), cause="stacked", legend.title="X1=0,X2=0.1", legend.legend=paste("cause:",getStates(ajfitX$model.response)), plot.main="Subject specific stacked plot") ``` ## See Also `plot`, `legend`, `axis`, `prodlim`,`plot.Hist`,`summary.prodlim`, `neighborhood`, `atRisk`, `confInt`, `markTime`, `backGround` ## Author(s) Thomas Alexander Gerds

plot.prodlim function