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

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Frank Harrell

Department of Biostatistics, Vanderbilt University