survreg {survival} | R Documentation |
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.
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, ...)
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" , "exponential" , "gaussian" ,
"logistic" ,"lognormal" and "loglogistic" .
Otherwise, it is assumed to be a user defined list conforming to the
format described in survreg.distributions .
|
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 <=0 then the scale is estimated. |
control |
a list of control values, in the format producted 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 .
|
an object of class survreg
is returned.
survreg.object
, survreg.distributions
,
pspline
, frailty
, ridge
# Fit an exponential model: these are all the same survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull',scale=1) survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist="exponential") # There are multiple ways to parameterize a Weibull distribution. The survreg # function imbeds it in a general location-scale familiy, 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, but with left censored data. tobinfit <- survreg(Surv(durable, durable>0, type='left') ~ age + quant, data=tobin, dist='gaussian')