On Sat, 16 Jul 2005, Kylie-Anne Richards wrote: > Thank you for your help. > ____________________________________________________________ >> In any case, to specify f.pom You need it to be a factor with the same set >> of levels. You don't say what the lowest level of pom is, but if it is, >> say, -3. >> >> f.pom=factor(-3, levels=seq(-3,2.5, by=0.5)) > ____________________________________________________________ > > For this particular model, f.pom starts at -5.5 going to 2 in 0.5 increments. > I seem to have misunderstood your explanation, as R is still producing an > error.
In the model you showed, there were no factor levels below -2.5. You need to make sure that the levels are the same in the initial data and the data supplied to survfit. Check this with levels(). > ____________________________________________________________ >> I would first note that the survival function at zero covariates is not a >> very useful thing and is usually numerically unstable, and it's often more >> useful to get the survival function at some reasonable set of covariates. > ____________________________________________________________ > > Please correct me if I'm wrong, I was under the impression that the survival > function at zero covariates gave the baseline distribution. I.e. if given the > baseline prob.,S_0, at time t, one could calculate the survival prob for > specified covariates by > S_0^exp(beta(vo)*specified(vo)+beta(po)*specified(po)+beta(f.pom at the level > of interest)) for time t. > > Since I was unable to get survfit to work with specified covariates, I was > using the survival probs of the 'avg' covariates, S(t), to determine the > baseline at time t, i.e. > S(t)^(1/exp(beta(vo)*mean(vo)+beta(po)*mean(po)+beta(f.pom-5.5)*mean(f.pom-5.5)+beta(f.pom-5.0)*mean(f.pom-5.0)+........). > > And then proceeding as mention in the above paragraph (clearly not an > efficient way of doing things). > Yes, but you don't need to go via the baseline. The survival curves for any two covariate vectors z1 and z2 are related by S(t; z1)= S(t; z2)^(z1-z2) For convenience of mathematical notation, mathematical statisticians write everything in terms of z2=0, and call this "the baseline". In the real world, though, you are better off with a baseline defined at a covariate value somewhere in the vicinity of the actual data. If, as if often the case, the zero covariate value is a long way from the observed data, both the computation of the survival curve at zero and the transformation to the covariates you want are numerically ill-conditioned. So, you can use the "baseline" returned by survfit(z2), which is at z2=fit$means, to do anything you can do with the baseline at z=0, and the computations will be more accurate. -thomas ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html