Problem plotting curve on survival curve

It is easier to get survival curves using the predict function.  Here is a
simple example:

tfit <- survreg(Surv(time, status) ~ factor(ph.ecog), data=lung)
tdata <- data.frame(ph.ecog=factor(0:3))
qpred <- predict(tfit, newdata= tdata, type='quantile', p=1:99/100)
matplot(t(qpred), 99:1/100, type='l')

Many thanks - that worked at treat... (One day I might work out what it 
does - for now I'm happy it does it!) In terms of when I write up what I 
did is this still a weibull regression?  help(predict.survreg) just 
calls it a quantile...  (Sorry that may be dumb question ;-) )

The above fit assumed a common shape for the 4 groups, 
you can add a "+ strata(ph.ecog)" term to have a separate scale for each group; 
this would give the same curves as 4 separate fits to the subgroups.

Any thoughts on which is scientifically more valid?  I'd have thoughts 4 
separate shapes?  Certainly if I'm modeling drugs - its surely possible 
that a new drug might change the course of disease and therefore the 
shape of the curve altogether?

Brings me back to my extra question - is there any way to determine 
quality of the fit for this (like an R^2 value for a linear regression). 
  That might answer if a strata approach is needed.