| 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,
survreg.old
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")