| survplot {Design} | R Documentation |
Plot estimated survival curves, and for parametric survival models, plot
hazard functions. There is an option to print the number of subjects
at risk at the start of each time interval. Curves are automatically
labeled at the points of maximum separation (using the labcurve
function), and there are many other options for labeling that can be
specified with the label.curves parameter. For example, different
plotting symbols can be placed at constant x-increments and a legend
linking the symbols with category labels can automatically positioned on
the most empty portion of the plot.
survplot(fit, ...)
## S3 method for class 'Design':
survplot(fit, ..., xlim,
ylim=if(loglog) c(-5, 1.5) else if
(what == "survival" & missing(fun)) c(0, 1),
xlab, ylab, time.inc,
what=c("survival","hazard"),
type=c("tsiatis","kaplan-meier"),
conf.type=c("log-log","log","plain","none"),
conf.int=FALSE, conf=c("bars","bands"),
add=FALSE, label.curves=TRUE,
abbrev.label=FALSE, lty, lwd=par("lwd"), col=1,
adj.subtitle, loglog=FALSE, fun,
n.risk=FALSE, logt=FALSE, dots=FALSE, dotsize=.003,
grid=FALSE, srt.n.risk=0, sep.n.risk=0.056, adj.n.risk=1,
y.n.risk, cex.n.risk=.6, pr=FALSE)
## S3 method for class 'survfit':
survplot(fit, xlim,
ylim, xlab, ylab, time.inc,
conf=c("bars","bands","none"), add=FALSE,
label.curves=TRUE, abbrev.label=FALSE,
lty,lwd=par('lwd'),col=1,
loglog=FALSE,fun,n.risk=FALSE,logt=FALSE,
dots=FALSE,dotsize=.003,
grid=FALSE,
srt.n.risk=0,sep.n.risk=.056,adj.n.risk=1,
y.n.risk,cex.n.risk=.6, pr=FALSE, ...)
fit |
result of fit (cph, psm, survfit, survest.psm)
|
... |
list of factors with names used in model. For fits from survfit, these
arguments do not appear - all strata are plotted. Otherwise the first factor
listed is
the factor used to determine different survival curves. Any other factors
are used to specify single constants to be adjusted to, when defaults given
to fitting routine (through limits) are not used.
The value given to factors is the original
coding of data given to fit, except that for categorical or strata
factors the text string levels may be specified. The form
of values given to the first factor are NA (use default range or list of
all values if variable is discrete), "text" if factor is categorical,
c(value1, value2, ...), or a function which returns a vector, such as
seq(low,high,by=increment). NA may be specified only for the first factor.
In this case the Low effect, Adjust to, and High effect values will
be used from datadist if the variable is continuous.
For variables not defined to datadist, you must specify non-missing
constant settings (or a vector of settings for the one displayed variable).
Note that since survfit objects do not use the variable list in ...,
you can specify any extra arguments to labcurve by adding them at the
end of the list of arguments.
|
xlim |
a vector of two numbers specifiying the x-axis range for follow-up time.
Default is (0,maxtime) where maxtime was the pretty()d version
of the maximum follow-up time
in any stratum, stored in fit$maxtime. If logt=TRUE,
default is (1, log(maxtime)).
|
ylim |
y-axis limits. Default is c(0,1) for survival, and c(-5,1.5) if loglog=TRUE.
If fun or loglog=TRUE are given and ylim is not,
the limits will be computed from the data. For what="hazard", default
limits are computed from the first hazard function plotted.
|
xlab |
x-axis label. Default is units attribute of failure time variable given to
Surv.
|
ylab |
y-axis label. Default is "Survival Probability" or
"log(-log Survival Probability)". If fun is given, the default
is "". For what="hazard", the default is "Hazard Function".
|
time.inc |
time increment for labeling the x-axis and printing numbers at risk.
If not specified, the value
of time.inc stored with the model fit will be used.
|
type |
specifies type of estimates, "tsiatis" (the default) or "kaplan-meier".
"tsiatis" here corresponds to the Breslow
estimator. This is ignored if survival estimates stored with surv=TRUE are
being used. For fits from survfit, this argument is also ignored, since
it is specified as an argument to survfit.
|
conf.type |
specifies the basis for confidence limits. If estimates stored with surv=TRUE
are being used, always uses "log-log", the default. This argument is
ignored for fits from survfit.
|
conf.int |
Default is FALSE. Specify e.g. .95 to plot 0.95 confidence bands.
For fits from parametric survival models, or Cox models with x=TRUE and y=TRUE
specified to the fit, the exact asymptotic formulas will be used to
compute standard errors, and confidence limits are based on log(-log S(t)).
If x=TRUE and y=TRUE were not specified to cph but surv=TRUE was, the
standard errors stored for the underlying survival curve(s) will be used.
These agree with the former if predictions are requested at the mean
value of X beta or if there are only stratification factors in the model.
This argument is ignored for fits from survfit, which must have previously
specified confidence interval specifications.
|
conf |
"bars" for confidence bars at each time.inc time point. If the fit
was from cph(..., surv=TRUE), the time.inc used will be that stored
with the fit. Use conf="bands" for bands using
standard errors at each failure time. For survfit objects only,
conf may also be "none", indicating that confidence interval
information stored with the survfit result should be ignored.
|
what |
defaults to "survival" to plot survival estimates. Set to "hazard" or
an abbreviation to plot the hazard function (for psm fits only).
Confidence intervals are not available for what="hazard".
|
add |
set to TRUE to add curves to an existing plot.
|
label.curves |
default is TRUE to use labcurve to label curves where they are farthest
apart. Set label.curves to a list to specify options to
labcurve, e.g., label.curves=list(method="arrow", cex=.8).
These option names may be abbreviated in the usual way arguments
are abbreviated. Use for example label.curves=list(keys=1:5)
to draw symbols (as in pch=1:5 - see points)
on the curves and automatically position a legend
in the most empty part of the plot. Set label.curves=FALSE to
suppress drawing curve labels. The col, lty, lwd, and type
parameters are automatically passed to labcurve, although you
can override them here. To distinguish curves by line types and
still have labcurve construct a legend, use for example
label.curves=list(keys="lines"). The negative value for the
plotting symbol will suppress a plotting symbol from being drawn
either on the curves or in the legend.
|
abbrev.label |
set to TRUE to abbreviate() curve labels that are plotted
|
lty |
vector of line types to use for different factor levels. Default is
c(1,3,4,5,6,7,...).
|
lwd |
vector of line widths to use for different factor levels. Default is
current par setting for lwd.
|
col |
color for curve, default is 1. Specify a vector to assign different
colors to different curves.
|
adj.subtitle |
set to FALSE to suppress plotting subtitle with levels of adjustment factors
not plotted. Defaults to TRUE if there are 4 or fewer adjustment factors.
This argument is ignored for survfit.
|
loglog |
set to TRUE to plot log(-log Survival) instead of Survival
|
fun |
specifies any function to translate estimates and confidence limits before plotting |
logt |
set to TRUE to plot log(t) instead of t on the x-axis
|
n.risk |
set to TRUE to add number of subjects at risk for each curve, using the
surv.summary created by cph or using the failure times used in
fitting the model if y=TRUE was specified to the fit or if the fit
was from survfit.
The numbers are placed at the bottom
of the graph unless y.n.risk is given.
If the fit is from survest.psm, n.risk does not apply.
|
srt.n.risk |
angle of rotation for leftmost number of subjects at risk (since this number
may run into the second or into the y-axis). Default is 0.
|
adj.n.risk |
justification for leftmost number at risk. Default is 1 for right
justification.
Use 0 for left justification, .5 for centered.
|
sep.n.risk |
multiple of upper y limit - lower y limit for separating lines of text
containing number of subjects at risk. Default is .056*(ylim[2]-ylim[1]).
|
y.n.risk |
When n.risk=TRUE, the default is to place numbers of patients at risk above
the x-axis. You can specify a y-coordinate for the bottom line of the
numbers using y.n.risk.
|
cex.n.risk |
character size for number of subjects at risk (when n.risk is TRUE)
|
dots |
set to TRUE to plot a grid of dots. Will be plotted at every time.inc (see
cph) and at survival increments of .1 (if d>.4), .05 (if .2 < d <= .4), or .025
(if d <= .2), where d is the range of survival displayed.
|
dotsize |
size of dots in inches |
grid |
defaults to FALSE. Set to a color shading to plot faint lines. Set to 1
to plot solid lines. Default is .05 if TRUE.
|
pr |
set to TRUE to print survival curve coordinates used in the plots
|
survplot will not work for Cox models with time-dependent covariables.
Use survest or survfit for that purpose.
Use ps.slide, win.slide, gs.slide to set up nice defaults for
plotting. These also set a system option mgp.axis.labels to allow x
and y-axes to have differing mgp graphical parameters (see par).
This is important when labels for y-axis tick marks are to be written
horizontally (par(las=1)), as a larger gap between the labels and
the tick marks are needed. You can set the axis-specific 2nd
component of mgp using mgp.axis.labels(c(xvalue,yvalue)).
list with components adjust (text string specifying adjustment levels)
and curve.labels (vector of text strings corresponding to levels of factor
used to distinguish curves). For survfit, the returned value is the
vector of strata labels, or NULL if there are no strata.
plots. If par()$mar[4]<4, issues par(mar=) to increment mar[4] by 2
if n.risk=TRUE and add=FALSE. The user may want to reset par(mar) in
this case to not leave such a wide right margin for plots. You usually
would issue par(mar=c(5,4,4,2)+.1).
datadist, Design, cph,
psm, survest, predict.Design,
plot.Design,
units, errbar,
survfit, survreg.distributions,
labcurve,
mgp.axis.labels, par,
ps.slide
# Simulate data from a population model in which the log hazard
# function is linear in age and there is no age x sex interaction
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('male','female'), n, TRUE))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)
#Plot stratified survival curves by sex, adj for quadratic age effect
# with age x sex interaction (2 d.f. interaction)
f <- cph(S ~ pol(age,2)*strat(sex), surv=TRUE)
#or f <- psm(S ~ pol(age,2)*sex)
survplot(f, sex=NA, n.risk=TRUE) #Adjust age to median
survplot(f, sex=NA, logt=TRUE, loglog=TRUE) #Check for Weibull-ness (linearity)
survplot(f, sex=c("male","female"), age=50)
#Would have worked without datadist
#or with an incomplete datadist
survplot(f, sex=NA, label.curves=list(keys=c(2,0), point.inc=2))
#Identify curves with symbols
survplot(f, sex=NA, label.curves=list(keys=c('m','f')))
#Identify curves with single letters
#Plots by quintiles of age, adjusting sex to male
options(digits=3)
survplot(f, age=quantile(age,seq(0,1,by=.2)), sex="male")
#Plot survival Kaplan-Meier survival estimates for males
f <- survfit(S, subset=sex=="male")
survplot(f)
#Plot survival for both sexes
f <- survfit(S ~ sex)
survplot(f)
#Check for log-normal and log-logistic fits
survplot(f, fun=qnorm, ylab="Inverse Normal Transform")
survplot(f, fun=function(y)log(y/(1-y)), ylab="Logit S(t)")
options(datadist=NULL)