```
survfit(formula, data, weights, subset, na.action,
newdata, individual=F, conf.int=.95, se.fit=T,
type=c("kaplan-meier","fleming-harrington", "fh2"),
error=c("greenwood","tsiatis"),
conf.type=c("log","log-log","plain","none"),
conf.lower=c("usual", "peto", "modified"))
basehaz(fit,centered=TRUE)
```

formula

A formula object or a

`coxph`

object.
If a formula object is supplied it must have a `Surv`

object as the
response on the left of the `~`

operator and, if desired, terms
separated by + operators on the right.
One of the data

a data frame in which to interpret the variables named in the formula,
or in the

`subset`

and the `weights`

argument.weights

The weights must be nonnegative and it is strongly recommended that
they be strictly positive, since zero weights are ambiguous, compared
to use of the

`subset`

argument.subset

expression saying that only a subset of the rows of the data
should be used in the fit.

na.action

a missing-data filter function, applied to the model frame, after any

`subset`

argument has been used.
Default is `options()$na.action`

.newdata

a data frame with the same variable names as those that appear
in the

`coxph`

formula. Only applicable when `formula`

is a `coxph`

object.
The curve(s) produced will be representative of a cohort who's
covariates correspoindividual

a logical value indicating whether the data frame represents different
time epochs for only one individual (T), or whether multiple rows indicate
multiple individuals (F, the default). If the former only one curve
will be produced; if the latter there wi

conf.int

the level for a two-sided confidence interval on the survival curve(s).
Default is 0.95.

se.fit

a logical value indicating whether standard errors should be
computed. Default is

`TRUE`

.type

a character string specifying the type of survival curve.
Possible values are

`"kaplan-meier"`

, `"fleming-harrington"`

or `"fh2"`

if a formula is given
and `"aalen"`

or `"kaplan-meier"`

if the first aerror

either the string

`"greenwood"`

for the Greenwood formula or
`"tsiatis"`

for the Tsiatis formula, (only the first character is
necessary). The default is `"tsiatis"`

when a `coxph`

object is
given, and it is conf.type

One of

`"none"`

, `"plain"`

, `"log"`

(the default), or `"log-log"`

. Only
enough of the string to uniquely identify it is necessary.
The first option causes confidence intervals not to be
generated. The second cconf.lower

controls modified lower limits to the curve,
the upper limit remains unchanged. The modified lower limit
is based on an 'effective n' argument. The confidence
bands will agree with the usual calculation at each death time, but unlike
the usual bands the

fit

a

`coxph`

objectcentered

Compute the baseline hazard at the covariate mean rather than at zero?

- a
`survfit`

object; see the help on`survfit.object`

for details. Methods defined for`survfit`

objects are provided for`print`

,`plot`

,`lines`

, and`points`

.For

`basehaz`

, a dataframe with the baseline hazard, times, and strata.

`exp(sum(coef*(x-center)))`

are used,
ignoring any value for `weights`

input by the user. There is also an extra
term in the variance of the curve, due to the variance ofthe coefficients and
hence variance in the computed weights.
The Greenwood formula for the variance is a sum of terms
d/(n*(n-m)), where d is the number of deaths at a given time point, n
is the sum of `weights`

for all individuals still at risk at that time, and
m is the sum of `weights`

for the deaths at that time. The
justification is based on a binomial argument when weights are all
equal to one; extension to the weighted case is ad hoc. Tsiatis
(1981) proposes a sum of terms d/(n*n), based on a counting process
argument which includes the weighted case.

The two variants of the F-H estimate have to do with how ties are handled.
If there were 3 deaths out of 10 at risk, then the first would increment
the hazard by 3/10 and the second by 1/10 + 1/9 + 1/8. For curves created
after a Cox model these correspond to the Breslow and Efron estimates,
respectively, and the proper choice is made automatically.
The `fh2`

method will give results closer to the Kaplan-Meier.

Based on the work of Link (1984), the log transform is expected to produce the most accurate confidence intervals. If there is heavy censoring, then based on the work of Dorey and Korn (1987) the modified estimate will give a more reliable confidence band for the tails of the curve.

Fleming, T. H. and Harrington, D.P. (1984). Nonparametric estimation of the
survival distribution in censored data. *Comm. in Statistics* 13, 2469-86.

Kalbfleisch, J. D. and Prentice, R. L. (1980).
*The Statistical Analysis of Failure Time Data.*
Wiley, New York.

Link, C. L. (1984). Confidence intervals for the survival
function using Cox's proportional hazards model with
covariates. *Biometrics* 40, 601-610.

Tsiatis, A. (1981). A large sample study of the estimate
for the integrated hazard function in Cox's regression
model for survival data. *Annals of Statistics* 9, 93-108.

`print.survfit`

, `plot.survfit`

, `lines.survfit`

, `summary.survfit`

,
`coxph`

, `Surv`

, `strata`

.```
#fit a Kaplan-Meier and plot it
data(aml)
fit <- survfit(Surv(time, status) ~ x, data=aml)
plot(fit)
# plot only 1 of the 2 curves from above
plot(fit[2])
#fit a cox proportional hazards model and plot the
#predicted survival curve
data(ovarian)
fit <- coxph( Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)
plot( survfit( fit))
```

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