FollowUpPlot.RdProduces a Follow-Up (or inverse Kaplan Meier Plot) comparing actual and ideal observation times. Patients with event are censored. Medians are indicated.
FollowUpPlot(DFR, TIMES, STATUS, IDEALTIMES, MAIN = "Distribution of ideal and actual observation times", XLAB = "follow-up time", YLAB = "proportion still under observation", UNITS = "months", COLS = c(3, 2), CEX = 1.2)
| DFR | data.frame with variables TIMES, STATUS, IDEALTIMES (colnames). Alternatively, if DFR set to NULL, TIMES, STATUS, IDEALTIMES will be treated as data vectors. |
|---|---|
| TIMES | name of times component of time to event, alternatively data vector |
| STATUS | name of status component of time to event, alternatively data vector |
| IDEALTIMES | name of ideal observation time component of time to event, alternatively data vector |
| MAIN | ="Distribution of ideal and actual observation times", plot title |
| XLAB | ="follow-up time", label for time axis |
| YLAB | ="proportion still under observation",label for y axis |
| UNITS | ="months", unit of time appendend at XLAB if not NULL or blank. In addition, 'months', 'Months', 'days', 'Days' are recognised and used to find appropriate ticks. |
| COLS | =c(3,2), colours for ideal and actual observation curves |
| CEX | =1.2, scaling factor of fonts on axes and legend |
returns data.frame with estimates and 95
FollowUpPlot
if (FALSE) { ### Test FollowUpPlot TIMES<-rexp(100)*12 TIMES[1:5]<--TIMES[1:5] STATUS<-rbinom(100,1,.2) IDEALTIMES<-runif(100,min=1,max=70) DFR<-data.frame(TIMES,STATUS,IDEALTIMES) FollowUpPlot(DFR=DFR,"TIMES","STATUS","IDEALTIMES",UNITS="months") FollowUpPlot(DFR=DFR,"TIMES","STATUS","IDEALTIMES",UNITS="years") FollowUpPlot(DFR=NULL,TIMES,STATUS,IDEALTIMES,UNITS="months",COLS=c(4,3)) FollowUpPlot(DFR=NULL,TIMES,STATUS,IDEALTIMES,UNITS="days",CEX=1.5) }