This class of objects is returned by the survreg function
to represent a fitted parametric survival model.
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 coefficients of the 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 non-estimable
coefficients.
coefficients of the baseline model, which will contain the intercept and log(scale), or multiple scale factors for a stratified model.
the variance-covariance matrix for the parameters, including the log(scale) parameter(s).
a vector of length 2, containing the log-likelihood for the baseline and full models.
the number of iterations required
the linear predictor for each subject.
the degrees of freedom for the final model. For a penalized model this will be a vector with one element per term.
the scale factor(s), with length equal to the number of strata.
degrees of freedom for the initial model.
a vector of the column means of the coefficient matrix.
the distribution used in the fit.
included for a weighted fit.
The object will also have the following components found in
other model results (some are optional):
linear predictors, weights, x, y, model,
call, terms and formula.
See lm.