coefficients
| the coefficients of the linear predictor, which multiply the columns of the model matrix. If the model is over-determined there will be missing values in the vector corresponding to the redundant columns in the model matrix. |
var
| the variance matrix of the coefficients. Rows and columns corresponding to any missing coefficients are set to zero. |
naive.var
|
this component will be present only if the robust option was true. If so,
the var component will contain the robust estimate of variance, and this
component will contain the ordinary estimate.
|
loglik
| a vector of length 2 containing the log-likelihood with the initial values and with the final values of the coefficients. |
score
| value of the efficient score test, at the initial value of the coefficients. |
iter
| number of iterations used. |
linear.predictors
| the vector of linear predictors, one per subject. |
residuals
| the martingale residuals. |
means
| vector of column means of the X matrix. Subsequent survival curves are adjusted to this value. |
n
| the number of observations used in the fit. |
weights
| the vector of case weights, if one was used. |
method
| the computation method used. |
robust.var
|
optional component added by the coxph.rvar function, containing a sandwich
estimate of variance.
|
rcall
|
optional copy of the call to coxph.rvar .
The object will also contain the following, for documentation see the |
coxph
object.
survfit
, coxph.detail
, cox.zph
, survreg
, residuals.coxph
.