coefficients
|
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 inestimable
coefficients.
|
parms
|
the parameters of the model that are not coefficients of the X matrix.
The first of these will always be log(sigma) .
|
fixed
|
a vector of the same length as parms , where 1 indicates a parameter that
was fixed at its starting value and was not part of the iteration.
|
deviance
| minus twice the difference between the maximized log-likelihood under the fitted model and a saturated model. Similar to the residual sum of squares. |
loglik
| the log-likelihood for the final model. |
null.deviance
|
the deviance corresponding to the model with only an itercept term, and
with parms fixed at their final values.
|
dresiduals
| the deviance residuals. |
var
| the final variance matrix, including both coefficients and free parameters. |
family
|
a 2 element character vector giving the name of the family and
the link; mainly for printing purposes.
The object will also have the components of an |
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.
survreg
, glm.object
, lm.object
.