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.For the case of a two stratum analysis by survfit,
survdiffplot plots the difference in two Kaplan-Meier estimates
along with approximate confidence bands for the differences, with a
reference line at zero. The number of subjects at risk is optionally
plotted. This number is taken as the minimum of the number of subjects
at risk over the two strata. When conf='diffbands',
survdiffplot instead does not make a new plot but adds a shaded
polygon to an existing plot, showing the midpoint of two survival
estimates plus or minus 1/2 the width of the confidence interval for the
difference of two Kaplan-Meier estimates.
survplot(fit, ...)
## S3 method for class 'rms':
survplot(fit, \dots, 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("bands","bars"),
add=FALSE, label.curves=TRUE,
abbrev.label=FALSE, levels.only=FALSE,
lty, lwd=par("lwd"),
col=1, col.fill=gray(seq(.95, .75, length=5)),
adj.subtitle=TRUE, loglog=FALSE, fun,
n.risk=FALSE, logt=FALSE, dots=FALSE, dotsize=.003,
grid=NULL, 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("bands","bars","diffbands","none"), add=FALSE,
label.curves=TRUE, abbrev.label=FALSE,
levels.only=FALSE, lty,lwd=par('lwd'),
col=1, col.fill=gray(seq(.95, .75, length=5)),
loglog=FALSE,fun,n.risk=FALSE,logt=FALSE,
dots=FALSE,dotsize=.003, grid=NULL,
srt.n.risk=0,sep.n.risk=.056,adj.n.risk=1,
y.n.risk,cex.n.risk=.6, pr=FALSE, ...)
survdiffplot(fit, order=1:2, fun=function(y) y,
xlim, ylim, xlab, ylab="Difference in Survival Probability",
time.inc, conf.int, conf=c("shaded", "bands","diffbands","none"),
add=FALSE, lty=1, lwd=par('lwd'), col=1,
n.risk=FALSE, grid=NULL,
srt.n.risk=0, adj.n.risk=1,
y.n.risk, cex.n.risk=.6)cph, psm, survfit,
survest.psm). For survdiffplot, fit must be the
result of survfit.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 (0,maxtime) where maxtime was the pretty()d version
of the maximum follow-up time
in any stratum, stored in fit$maxtimec(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 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. Fosurvfit.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 "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" (the default) for
bands "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=.TRUE to abbreviate() curve labels that are plottedTRUE to remove variablename= from the start of
curve labels.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.
This argument is ignored for survfit.TRUE to plot log(-log Survival) instead of SurvivalTRUE to plot log(t) instead of t on the x-axisTRUE 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
w0.1 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<="" code="">), or .025 (if d <= .2<="" code="">),
wherNULL (not drawing grid lines). Set to TRUE to
plot gray(.8) grid lines, or specify any color.TRUE to print survival curve coordinates used in the plots1:2 indicates
that the second group is subtracted from the first. Specify
order=2:1 to instead subtract thcurve.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.par()$mar[4]<4< code="">, 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).survplot will not work for Cox models with time-dependent covariables.
Use survest or survfit for that purpose.There is a 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)).
datadist, rms, cph,
psm, survest, predictrms,
plot.Predict,
units, errbar,
survfit, survreg.distributions,
labcurve,
mgp.axis, par,# 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)
# When age is in the model by itself and we predict at the mean age,
# approximate confidence intervals are ok
f <- cph(S ~ age, surv=TRUE)
survplot(f, age=mean(age), conf.int=.95)
g <- cph(S ~ age, x=TRUE, y=TRUE)
survplot(g, age=mean(age), conf.int=.95, add=TRUE, col='red', conf='bars')
# Repeat for an age far from the mean; not ok
survplot(f, age=75, conf.int=.95)
survplot(g, age=75, conf.int=.95, add=TRUE, col='red', conf='bars')
#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), x=TRUE, y=TRUE)
#or f <- psm(S ~ pol(age,2)*sex)
Predict(f, sex, age=c(30,50,70))
survplot(f, sex, n.risk=TRUE, levels.only=TRUE) #Adjust age to median
survplot(f, sex, 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, label.curves=list(keys=c(2,0), point.inc=2))
#Identify curves with symbols
survplot(f, sex, 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,(1:4)/5), sex="male")
#Plot survival Kaplan-Meier survival estimates for males
f <- survfit(S ~ 1, 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)")
#Plot the difference between sexes
survdiffplot(f)
#Similar but show half-width of confidence intervals centered
#at average of two survival estimates
survplot(f, conf='diffbands')
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