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
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