the vector of coefficients. If the model is over-determined there will be missing values in the vector corresponding to the redundant columns in the model matrix.

the variance matrix of the coefficients. Rows and columns corresponding to any missing coefficients are set to zero.

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. (A far better name would be asymp.var since it contains the model-based asympotitic variance estimate, which is not necessarily "naive"; but that ship has sailed.)

a vector of length 2 containing the log-likelihood with the initial values and with the final values of the coefficients.

value of the efficient score test, at the initial value of the coefficients.

the robust log-rank statistic, if a robust variance was requested.

the Wald test of whether the final coefficients differ from the initial values.

number of iterations used.

the vector of linear predictors, one per subject. Note that this vector has been centered, see predict.coxph for more details.

the martingale residuals.

vector of values used as the reference for each covariate. For instance, a later call to predict(fit, type='risk') will give the hazard ratio between an observation and this reference. (For most covariates this will contain the mean.)

the number of observations used in the fit.

the number of events (usually deaths) used in the fit.

if the call had an id argument, the number of unique id values

a vector of length 6, containing the number of pairs that are concordant, discordant, tied on x, tied on y, and tied on both, followed by the standard error of the concordance statistic.

the first derivative vector at the solution.

the vector of case weights, if one was used.

the method used for handling tied survival times.

the na.action attribute, if any, that was returned by the na.action routine.

the value of the timefix option used in the fit

The object will also contain the following, for documentation see the lm object: terms, assign, formula, call, and, optionally, x, y, and/or frame.