# survplot

##### Plot Survival Curves and Hazard Functions

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.

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.

- Keywords
- models, hplot, nonparametric, survival

##### Usage

```
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, 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=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("bands","bars","none"), add=FALSE,
label.curves=TRUE, abbrev.label=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=FALSE,
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,
xlim, ylim, xlab, ylab="Difference in Survival Probability",
time.inc, conf.int=.95,
conf=c("shaded", "bands","none"),
add=FALSE, lty=1, lwd=par('lwd'), col=1,
n.risk=FALSE, grid=FALSE,
srt.n.risk=0, adj.n.risk=1,
y.n.risk, cex.n.risk=.6)
```

##### Arguments

- fit
- result of fit (
`cph`

,`psm`

,`survfit`

,`survest.psm`

). For`survdiffplot`

,`fit`

must be the result of`survfit`

. - ...
- 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 - 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`

- 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 < - 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. Fo - conf.type
- specifies the basis for confidence limits. 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 - 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"`

(the default) for bands- 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=.`

- 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. - col.fill
- a vector of colors to used in filling confidence bands
- adj.subtitle
- set to
`FALSE`

to suppress plotting subtitle with levels of adjustment factors not plotted. Defaults to`TRUE`

. 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 w - 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<="" code="">), or .025 (if`

`d <= .2<="" code="">), where <`

- 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 - order
- an integer vector of length two specifying the order of groups when
computing survival differences. The default of
`1:2`

indicates that the second group is subtracted from the first. Specify`order=2:1`

to instead subtract th

##### Details

`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))`

.

##### Value

- 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.

##### Side Effects

plots. If `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)`

.

##### See Also

`datadist`

, `rms`

, `cph`

,
`psm`

, `survest`

, `predictrms`

,
`plot.Predict`

,
`units`

, `errbar`

,
`survfit`

, `survreg.distributions`

,
`labcurve`

,
`mgp.axis.labels`

, `par`

,
`ps.slide`

##### Examples

```
# 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) #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)
options(datadist=NULL)
```

*Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)*