survreg
Fit a parametric survival regression model. These are locationscale models for an arbitrary transform of the time variable; the most common cases use a log transformation, leading to accelerated failure time models.
 Keywords
 survival
Usage
survreg(formula, data, weights, subset,
na.action, dist="weibull", init=NULL, scale=0,
control,parms=NULL,model=FALSE, x=FALSE,
y=TRUE, robust=FALSE, score=FALSE, …)
Arguments
 formula

a formula expression as for other regression models.
The response is usually a survival object as returned by the
Surv
function. See the documentation forSurv
,lm
andformula
for details.  data

a data frame in which to interpret the variables named in
the
formula
,weights
or thesubset
arguments.  weights
 optional vector of case weights
 subset
 subset of the observations to be used in the fit
 na.action

a missingdata filter function, applied to the model.frame, after any
subset
argument has been used. Default isoptions()\$na.action
.  dist

assumed distribution for y variable.
If the argument is a character string, then it is assumed to name an
element from
survreg.distributions
. These include"weibull"
,"exponential"
,"gaussian"
,"logistic"
,"lognormal"
and"loglogistic"
. Otherwise, it is assumed to be a user defined list conforming to the format described insurvreg.distributions
.  parms
 a list of fixed parameters. For the tdistribution for instance this is the degrees of freedom; most of the distributions have no parameters.
 init
 optional vector of initial values for the parameters.
 scale
 optional fixed value for the scale. If set to <=0 then the scale is estimated.
 control

a list of control values, in the format produced by
survreg.control
. The default value issurvreg.control()
 model,x,y
 flags to control what is returned. If any of these is true, then the model frame, the model matrix, and/or the vector of response times will be returned as components of the final result, with the same names as the flag arguments.
 score
 return the score vector. (This is expected to be zero upon successful convergence.)
 robust
 Use robust 'sandwich' standard errors, based on
independence of individuals if there is no
cluster()
term in the formula, based on independence of clusters if there is.  …

other arguments which will be passed to
survreg.control
.
Details
All the distributions are cast into a locationscale framework, based on chapter 2.2 of Kalbfleisch and Prentice. The resulting parameterization of the distributions is sometimes (e.g. gaussian) identical to the usual form found in statistics textbooks, but other times (e.g. Weibull) it is not. See the book for detailed formulas.
Value
an object of class survreg
is returned.
References
Kalbfleisch, J. D. and Prentice, R. L., The statistical analysis of failure time data, Wiley, 2002.
See Also
survreg.object
, survreg.distributions
,
pspline
, frailty
, ridge
Examples
# Fit an exponential model: the two fits are the same
survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull',
scale=1)
survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian,
dist="exponential")
#
# A model with different baseline survival shapes for two groups, i.e.,
# two different scale parameters
survreg(Surv(time, status) ~ ph.ecog + age + strata(sex), lung)
# There are multiple ways to parameterize a Weibull distribution. The survreg
# function embeds it in a general locationscale family, which is a
# different parameterization than the rweibull function, and often leads
# to confusion.
# survreg's scale = 1/(rweibull shape)
# survreg's intercept = log(rweibull scale)
# For the loglikelihood all parameterizations lead to the same value.
y < rweibull(1000, shape=2, scale=5)
survreg(Surv(y)~1, dist="weibull")
# Economists fit a model called `tobit regression', which is a standard
# linear regression with Gaussian errors, and left censored data.
tobinfit < survreg(Surv(durable, durable>0, type='left') ~ age + quant,
data=tobin, dist='gaussian')