R: Regression for a Parametric Survival Model

survreg {survival}

R Documentation

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"`, `"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 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.

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 scales
survreg(Surv(time, status) ~ ph.ecog + age + strata(sex), lung)

# 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, and left censored data.
tobinfit <- survreg(Surv(durable, durable>0, type='left') ~ age + quant,
	            data=tobin, dist='gaussian')