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
.