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survival (version 2.44-1.1)

plot.survfit: Plot method for survfit objects

Description

A plot of survival curves is produced, one curve for each strata. The log=T option does extra work to avoid log(0), and to try to create a pleasing result. If there are zeros, they are plotted by default at 0.8 times the smallest non-zero value on the curve(s).

Curves are plotted in the same order as they are listed by print (which gives a 1 line summary of each). This will be the order in which col, lty, etc are used.

Usage

# S3 method for survfit
plot(x, conf.int=, mark.time=FALSE, 
 pch=3, col=1, lty=1, lwd=1, cex=1, log=FALSE, xscale=1, yscale=1,  
 xmax, ymin=0, fun, 
 xlab="", ylab="", xaxs="r",  conf.times, conf.cap=.005,
 conf.offset=.012, mark, …)

Arguments

x

an object of class survfit, usually returned by the survfit function.

conf.int

determines whether confidence intervals will be plotted. The default is to do so if there is only 1 curve, i.e., no strata.

mark.time

controls the labeling of the curves. If set to FALSE, no labeling is done. If TRUE, then curves are marked at each censoring time. If mark is a numeric vector then curves are marked at the specified time points.

pch

vector of characters which will be used to label the curves. The points help file contains examples of the possible marks. A single string such as "abcd" is treated as a vector c("a", "b", "c", "d"). The vector is reused cyclically if it is shorter than the number of curves. If it is present this implies mark.time = TRUE.

col

a vector of integers specifying colors for each curve. The default value is 1.

lty

a vector of integers specifying line types for each curve. The default value is 1.

lwd

a vector of numeric values for line widths. The default value is 1.

cex

a numeric value specifying the size of the marks. This is not treated as a vector; all marks have the same size.

log

a logical value, if TRUE the y axis wll be on a log scale. Alternately, one of the standard character strings "x", "y", or "xy" can be given to specific logarithmic horizontal and/or vertical axes.

yscale

a numeric value used to multiply the labels on the y axis. A value of 100, for instance, would be used to give a percent scale. Only the labels are changed, not the actual plot coordinates, so that adding a curve with "lines(surv.exp(...))", say, will perform as it did without the yscale argument.

xscale

a numeric value used like yscale for labels on the x axis. A value of 365.25 will give labels in years instead of the original days.

xmax

the maximum horizontal plot coordinate. This can be used to shrink the range of a plot. It shortens the curve before plotting it, so that unlike using the xlim graphical parameter, warning messages about out of bounds points are not generated.

ymin

lower boundary for y values. Survival curves are most often drawn in the range of 0-1, even if none of the curves approach zero. The parameter is ignored if the fun argument is present, or if it has been set to NA.

fun

an arbitrary function defining a transformation of the survival curve. For example fun=log is an alternative way to draw a log-survival curve (but with the axis labeled with log(S) values), and fun=sqrt would generate a curve on square root scale. Five often used transformations can be specified with a character argument instead: "S" gives the usual survival curve, "log" is the same as using the log=T option, "event" or "F" plots the empirical CDF \(F(t)= 1-S(t)\) (f(y) = 1-y), "cumhaz" plots the cumulative hazard function (f(y) = -log(y)), and "cloglog" creates a complimentary log-log survival plot (f(y) = log(-log(y)) along with log scale for the x-axis). The terms "identity" and "surv" are allowed as synonyms for type="S".

xlab

label given to the x-axis.

ylab

label given to the y-axis.

xaxs

either "S" for a survival curve or a standard x axis style as listed in par; "r" (regular) is the R default. Survival curves have historically been displayed with the curve touching the y-axis, but not touching the bounding box of the plot on the other 3 sides, Type "S" accomplishes this by manipulating the plot range and then using the "i" style internally. The "S" style is becoming increasingly less common, however.

conf.times

optional vector of times at which to place a confidence bar on the curve(s). If present, these will be used instead of confidence bands.

conf.cap

width of the horizontal cap on top of the confidence bars; only used if conf.times is used. A value of 1 is the width of the plot region.

conf.offset

the offset for confidence bars, when there are multiple curves on the plot. A value of 1 is the width of the plot region. If this is a single number then each curve's bars are offset by this amount from the prior curve's bars, if it is a vector the values are used directly.

mark

a historical alias for pch

for future methods

Value

a list with components x and y, containing the coordinates of the last point on each of the curves (but not the confidence limits). This may be useful for labeling.

Details

When the survfit function creates a multi-state survival curve the resulting object also has class `survfitms'. Competing risk curves are a common case. The only difference in the plots is that multi-state defaults to a curve that goes from lower left to upper right (starting at 0), where survival curves by default start at 1 and go down. All other options are identical.

When the conf.times argument is used, the confidence bars are offset by conf.offset units to avoid overlap. The bar on each curve are the confidence interval for the time point at which the bar is drawn, i.e., different time points for each curve. If curves are steep at that point, the visual impact can sometimes substantially differ for positive and negative values of conf.offset.

See Also

points.survfit, lines.survfit, par, survfit

Examples

Run this code
# NOT RUN {
leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) 
plot(leukemia.surv, lty = 2:3) 
legend(100, .9, c("Maintenance", "No Maintenance"), lty = 2:3) 
title("Kaplan-Meier Curves\nfor AML Maintenance Study") 
lsurv2 <- survfit(Surv(time, status) ~ x, aml, type='fleming') 
plot(lsurv2, lty=2:3, fun="cumhaz", 
	xlab="Months", ylab="Cumulative Hazard") 
# }

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