Plot of the estimated log hazard ratio in R Is it possible to plot the estimated log hazard ratio in R?
For example,
require(survival)
fit1 = coxph(Surv(futime, fustat) ~ rx, ovarian) 

 A: Not having a copy of Applied Survival Analysis, I'm guessing you're looking for something like this: http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Design:hazard.ratio.plot
A: Saw this question while browsing through the survival questions and though I just post some of my personal favorites. I like using the rms package for survival functions and you could do a forestplot type of output:
library(survival)
library(rms)
ddist <- datadist(ovarian)
options(datadist='ddist')

ovarian$rx <- factor(ovarian$rx)
fit1 = cph(Surv(futime, fustat) ~ rx + rcs(age, 3), ovarian, x=T, y=T)
# The plot.summary.rms
plot(summary(fit1, age=c(50,60)), q=c(.6, .8, .95), log=T, 
                 col=c("orange", "gold", "blue"))

gives you:

I also like the termplot that I've updated slightly:
par(mfrow=c(1,2))
termplot2(fit1, se=T, rug.type="density", rug=T, density.proportion=.05, 
          se.type="polygon", 
          ylab=rep("Hazard Ratio", times=2),
          main=rep("cph() plot", times=2),
          col.se=rgb(.2,.2,1,.4), col.term="black")

that gives you this plot:

and last but not least for a log-log plot if you wanted to look for the hazard over time as the comment suggested:
f <- survfit(Surv(futime, fustat) ~ rx, data=ovarian)
survplot(f, loglog=T, logt=T, xlab="log(Years)")

that gives:

another efficient way of looking at the hazard of time is the Schoenfeld residuals:
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
f <- cox.zph(fit1)
plot(f, resid=F)

that gives:

