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
Joint confidence interval for fractional polynomial terms
2 messages · Eleni Rapsomaniki, Frank E Harrell Jr
This does not exactly answer your question but if you were to use restricted cubic splines instead of FPs, an upcoming new release of the rms package allows one to easily obtain simultaneous confidence bands for any series of predictions. So for your case you would hold all covariates constant except for the one varying on the x-axis, and tell the Predict function to use conf.type='simultaneous'. I've also added pointwise bootstrap nonparametric confidence bands for this context. Simultaneous confidence bands handle the simultaneous uncertainty of all the terms making up the spline function that are "in play" for the range of predictions being requested, i.e., terms that are involved because of the knot locations and the Xs being requested. Note that with fractional polynomials, if you use any term selection, the pointwise confidence intervals are not even correct. Frank Eleni Rapsomaniki-3 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
______________________________________________ R-help@ 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.
----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Joint-confidence-interval-for-fractional-polynomial-terms-tp4278494p4278546.html Sent from the R help mailing list archive at Nabble.com.