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