The Cox proportional hazards model can be described as follows:
$$h(t|X)=h_{0}(t)e^{\beta X}$$
where $h(t)$ is the hazard rate at time $t$, $h_{0}(t)$ is the baseline hazard rate at time $t$, $\beta$ is a vector of coefficients and $X$ is a vector of covariates.
As you will know, the Cox model is a semi-parametric model in that it is only partially defined parametrically. Essentially, the covariate part assumes a functional form whereas the baseline part has no parametric functional form (it's form is that of a step function).
Additionally, the survival curve of the Cox model is:
$$\begin{align}
S(t|X)&=\text{exp}\bigg(-\int_{0}^{t}h_{0}(t)e^{\beta X}\,dt\bigg)\\
&=\text{exp}\big(-H_{0}(t)\big)^{\text{exp}(\beta X)}\\
&=S_{0}(t)^{\text{exp}(\beta X)}
\end{align}$$
where $H_{0}(t)=\int_{0}^{t}h_{0}(t)\,dt$, $S_{0}(t)=\text{exp}\big(-H_{0}(t)\big)$, $S(t)$ is the survival function at time $t$, $S_{0}(t)$ is the baseline survival function at time $t$ and $H_{0}(t)$ is the baseline cumulative hazard function at time $t$.
The R function basehaz() provides the estimated cumulative hazard function, $H_{0}(t)$, defined above. For example, I can fit a Cox PH model with a single covariate sexn in R as follows:
f=formula(sv~factor(sexn))
cox.fit=coxph(f)
I can then extract (and plot) the underlying baseline cumulative hazard function as follows:
bh=basehaz(cox.fit)
plot(bh[,2],bh[,1],main="Cumulative hazard function",xlab="Time",ylab="H0(t)")
Now, because of the proportional nature of the Cox model, to obtain the survival curves of the two groups defined by their sexn value I can just raise the cumulative hazard function to the power of the estimated coefficient for sexn.
For example, for my variable $sexn=\{0,1\}$, the two survival curves would be:
$$S(t|X=0)=\text{exp}(-H_{0}(t))^{\text{exp}(\beta(0))}=\text{exp}(-H_{0}(t))$$
and
$$S(t|X=1)=\text{exp}(-H_{0}(t))^{\text{exp}(\beta(1))}=\text{exp}(-H_{0}(t))^{\text{exp}(\beta)}$$
If you want to see the relative survival, you can just plot the curves as follows:
plot(bh[,2],exp(-bh[,1])^(exp(cox.fit$coef)),xlim=c(40,85),ylim=c(0,1),
col="red",main="Survival curves for two groups",xlab="Time",ylab="S(t|X)")
par(new=TRUE)
plot(bh[,2],exp(-bh[,1]),xlim=c(40,85),ylim=c(0,1),
col="blue",main="Survival curves for two groups",xlab="Time",ylab="S(t|X)")
legend("topright",c("sexn=1","sexn=0"),lty=c(1,1),col=c(2,4))
Thus, you can see that the group with $sexn=1$ has a lower survival than the group with $sexn=0$. If you want to measure the relative survival of the two groups you can do so in many ways. You can say that for two individuals (differing in only $sexn$) that start at $\text{Time}=40$, the individual with $sexn=1$ has a lower probability of surviving to any time $t>40$ compared with the individual with $sexn=0$.
I believe what you are trying to achieve is to calculate the survival estimate:
$$S(t=30|X)$$
This can be achieved by fitting a Cox model to a given survival object and applying the estimated coefficients to each individual depending on their individual covariates. This will scale the baseline survival curve and give you the desired survival estimate for each of your individuals.
Best Answer
A Cox model was explicitly designed to be able to estimate the hazard ratios without having to estimate the baseline hazard function. This is a strength and a weakness. The strength is that you cannot make errors in functions you don't estimate. This is a real strength and is the reason why people refer to it as "semi-parametric" and is to a large extent responsible for its popularity. However, it is also a real weakness, in that once you want to know something other than the hazard ratio, you will often require the baseline hazard function and that defeats the very purpose of a Cox model.
So I tend to use Cox models only when I am interested in hazard ratios and nothing else. If I want to know other things, I typically move on to other models like the ones discussed here: http://www.stata.com/bookstore/flexible-parametric-survival-analysis-stata/