survreg {survival5} | R Documentation |
Regression for a parametric survival model
survreg(formula, data=sys.parent(), subset, na.action, dist="weibull", init, scale=0, control, model=F, x=F, y=T, ...)
formula |
a formula expression as for other regression models.
See the documentation for lm and formula for details.
|
data |
optional data frame in which to interpret the variables occurring in the formula. |
subset |
subset of the observations to be used in the fit. |
na.action |
function to be used to handle any NAs in the data. |
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 this
standard.
|
parm |
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 .
|
model |
if TRUE, the model frame is returned. |
x |
if TRUE, then the X matrix is returned. |
y |
if TRUE, then the y vector (or survival times) is returned. |
... |
other arguments which will be passed to survreg.control .
|
an object of class survreg
is returned.
This routine underwent significant changes from survival4 to survival5. The survreg.old function gives a backwards-compatible interface.
The routine uses a Newton-Raphson iteration with step halving, with provision for general penalized term. Fisher scoring is used for intermediate steps where the information matrix is not positive definite.
survreg.object
, survreg.distributions
,
pspline
, frailty
, ridge
data(ovarian) ## 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") survreg.old(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='extreme',fixed=list(scale=1),link="log")