Mike,
Do something like:
require(rms)
dd <- datadist(mydatarame); options(datadist='dd')
f <- Rq(y ~ rcs(age,4)*sex, tau=.5) # use rq function in quantreg
summary(f) # inter-quartile-range differences in medians of y (b/c tau=.5)
plot(Predict(f, age, sex)) # show age effect on median as a continuous
variable
For more help type ?summary.rms and ?Predict
Frank
------------
When performing quantile regression (r package I used quantreg), the
value of the quantile refers to the quantile value of the dependent
variable.
Typically when trying to predict, since the information we have are the
independent variables, I am interested in trying to estimate the
coefficients based on the quantile values of the independent variables'
distribution. So that I can get an understanding, for certain ranges of
the predictor/independent variable values, the (target/dependent
variable) has (a certain level of exposure to the
predictors)/(coefficients).
Is there any way I can achieve that?
Just in case, if I am incorrect about my understanding on the way
quantiles are interpreted when using the package quantreg, please let me
know.
Thanks
Mike
--
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
