survreg function is deprecated; please use
instead.
Classes of objects that result from fitting a parametric survival model.
This class of objects is returned by the
survreg function
to represent a fitted parametric survival model.
Class
survreg inherits from class
glm, since it is fit by iterative
reweighted least squares; the object returned has all the components of a
weighted least squares object.
Objects of this class have methods for the functions
print,
summary
,
predict, and
residuals.
The following components must be included in a legitimate
survreg object.
The residuals, fitted values, coefficients and effects should be extracted
by the generic functions of the same name, rather than
by the
"$" operator.
linear.predictors, which multiply the
columns of the model matrix. It does not include the estimate of error
(sigma). The names of the coefficients are the names of the
single-degree-of-freedom effects (the columns of the model matrix).
If the model is over-determined there will be missing values in the
coefficients corresponding to nonestimable coefficients.
log(sigma).
parms, where 1 indicates a parameter that
was fixed at its starting value and was not part of the iteration.
parms fixed at their final values.
glm object not mentioned
above:
linear predictors,
fitted.values,
residuals,
effects,
R,
rank,
assign,
df.residual,
weights,
call,
iter,
contrasts,
terms and
formula. See
glm.object.