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, ...)
cph
,
psm
,
survfit
,
survest.psm
)
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.
(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))
.
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.
units
attribute of failure time variable given to
Surv
.
"Survival Probability"
or
"log(-log Survival Probability)"
. If
fun
is given, the default
is
""
. For
what="hazard"
, the default is
"Hazard Function"
.
time.inc
stored with the model fit will be used.
"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
.
surv=TRUE
are being used, always uses
"log-log"
, the default. This argument is
ignored for fits from
survfit
.
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.
"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.
"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"
.
TRUE
to add curves to an existing plot.
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.
TRUE
to
abbreviate()
curve labels that are plotted
c(1,3,4,5,6,7,...)
.
par
setting for
lwd
.
1
. Specify a vector to assign different
colors to different curves.
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
.
TRUE
to plot
log(-log Survival)
instead of
Survival
TRUE
to plot
log(t)
instead of
t
on the x-axis
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.
0
.
1
for right
justification.
Use
0
for left justification,
.5
for centered.
.056*(ylim[2]-ylim[1])
.
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
.
n.risk
is
TRUE
)
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.
FALSE
. Set to a color shading to plot faint lines. Set to
1
to plot solid lines. Default is
.05
if
TRUE
.
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))
.
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.
# 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)