Compute the predicted survival curve for a Cox model.

`basehaz(fit, newdata, centered=TRUE)`

a data frame with variable names of `hazard`

, `time`

and
optionally `strata`

. The first is actually the cumulative hazard.

- fit
a coxph fit

- newdata
a data frame containing one row for each predicted survival curve, said row contains the covariate values for that curve

- centered
ignored if the

`newdata`

argument is present. Otherwise, if TRUE return data from a predicted survival curve for the covariate values`fit$mean`

, if FALSE return a prediction for all covariates equal to zero.

This function is an alias for `survfit.coxph`

, which does the
actual work and has a richer set of options.
Look at that help file for more discussion and explanation.
This alias exists primarily because some users look for predicted survival
estimates under this name.

The function returns a data frame containing the `time`

,
`cumhaz`

and optionally the strata (if the fitted Cox model used
a strata statement), which are copied from the `survfit`

result.

If H(t; z) is the predicted cumulative hazard for an observation with
covariate vector z, then H(t;x) = H(t;z) r(x,z)
where r(x,z)= exp(beta[1](x[1]- z[1]) + beta[2](x[2]-z[2]) + ...) =
`exp(sum(coef(fit) * (x-z)))`

is the
Cox model's hazard ratio for covariate vector x vs covariate vector z.
That is,
the cumulative hazard H for a single reference value z is sufficient to
provide the hazard for any covariate values.
The predicted survival curve is S(t; x)= exp(-H(t;x)).
There is not a simple transformation for the variance of H, however.

Many textbooks refer to H(t; 0) as "the" baseline hazard
for a Cox model; this is returned by the `centered= FALSE`

option.
However, due to potential overflow or underflow in the exp() function
this can be a very bad idea in practice. The authors do not recommend
this option, but for users who insist: caveat emptor.
Offset terms can pose a particular challenge for the underlying code
and are always recentered; to override this use the newdata argument
and include the offset as one of the variables.

`survfit.coxph`