survival (version 2.38-3)

survreg: Regression for a Parametric Survival Model

Description

Fit a parametric survival regression model. These are location-scale models for an arbitrary transform of the time variable; the most common cases use a log transformation, leading to accelerated failure time models.

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 for Surv, lm and formula for details.

data

a data frame in which to interpret the variables named in the formula, weights or the subset arguments.

weights

optional vector of case weights

subset

subset of the observations to 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.

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", "expo

parms

a list of fixed parameters. For the t-distribution 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

control

a list of control values, in the format produced by survreg.control. The default value is survreg.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.

Value

• an object of class survreg is returned.

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 location-scale 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 log-likelihood 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')