Dear Professor Harrell, Once again thank you for your helpful reply. I could use rcs instead, so I look forward to your latest rms release (soon I hope?) Initially I favoured fractional polynomials thinking that the model would be easier to present, but now I see that with either method unless one plots the fitted function results are just as hard to interpret. That's why the simultaneous CI plot will be very useful.
Eleni > On Jan 9, 2012, at 8:45 AM, "Eleni Rapsomaniki" <[email protected]> > wrote: > >> Dear R users, >> >> The package 'mfp' that fits fractional polynomial terms to predictors. >> Example: >> data(GBSG) >> f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) >> + fp(prm, df = 4, select = 0.05), family = cox, data = >> GBSG) >> print(f) >> >> To describe the association between the original predictor, eg. age and >> risk for different values of age I can plot it the polynomials and >> fitted >> coefficients as: >> >> plot(0.407*I((age/100)^-2) + -4.96*I((age/100)^-0.5) ~ age, GBSG) >> >> But I can't work out how to get a 95% confidence interval for this >> curve... Any suggestions? I could bootstrap it, but is there a >> mathematical solution? >> >> Many thanks >> Eleni Rapsomaniki >> Medical Statistician >> UCL, London >> >> ______________________________________________ >> [email protected] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
