[R] Is there an equivalent to predict(..., type="linear") of a Proportional hazard model for a Cox model instead?

[Ben Rhelp]

Hi all,

Is there an equivalent to predict(...,type="linear") of a Proportional hazard 
model for a Cox model instead?

For example, the Figure 13.12 in MASS (p384) is produced by:

(aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + 
pspline(age, df=6), data = Aidsp))

zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels = 
levels(Aidsp$state)),
    T.categ = factor(rep("hs", 83), levels = levels(Aidsp$T.categ)), age = 
0:82), se = T, type = "linear")
plot(0:82, exp(zz$fit)/365.25, type = "l", ylim = c(0, 2), xlab = "age", ylab = 
"expected lifetime (years)")
lines(0:82, exp(zz$fit+1.96*zz$se.fit)/365.25, lty = 3, col = 2)
lines(0:82, exp(zz$fit-1.96*zz$se.fit)/365.25, lty = 3, col = 2)
rug(Aidsp$age + runif(length(Aidsp$age), -0.5, 0.5), ticksize = 0.015)

Is it possible to achieve something similar with a Cox model instead?

Is there a more detailed explanation of the "type" option for predict.coxph than 
what's in the help of predict.coxph? e.g. type=c("lp", "risk", "expected", 
"terms")

thanks in advance,

Ben