Thanks for the suggestions, Gunter.
On Wed, Jun 24, 2015 at 10:33 AM, Bert Gunter <bgunter.4...@gmail.com> wrote: > Not an answer to your question, but you should not be using "dummy" > variables in R. Use factors instead. Please read a R tutorial or text > -- there are many -- to learn how to fit models in R. You might also > wish to consult a local statistician or post on a statistics list like > stats.stackexchange.com for statistics questions, which are off topic > here. > > Further, when you post here, please read and follow the posting guide > (below) and post in plain text, not HTML. > > Cheers, > Bert > Bert Gunter > > "Data is not information. Information is not knowledge. And knowledge > is certainly not wisdom." > -- Clifford Stoll > > > On Wed, Jun 24, 2015 at 3:27 AM, James Shaw <sha...@gmail.com> wrote: >> I am interested in using quantile regression to fit the following model at >> different quantiles of a response variable: >> >> (1) y = b0 + b1*g1 + b2*g2 + B*Z >> >> where b0 is an intercept, g1 and g2 are dummy variables for 2 of 3 >> independent groups, and Z is a matrix of covariates to be adjusted for in >> the estimation (e.g., age, gender). The problem is that estimates for g2 >> and g1 are not estimable at all quantiles. To overcome this, one option is >> to fit a separate model for each group (i.e., group 0, which is reflected >> by intercept above, group 1, and group 2): >> >> (2) y = b11 + B1*Z (model for group 0) >> (3) y = b12 + B2*Z (model for group 1) >> (4) y = b13 + B3*Z (model for group 2) >> >> This would correspond to fitting a single model in which group membership >> was interacted with all covariates, albeit some of the interaction terms >> would not be estimable for the reason noted above. However, I ultimately >> would like to base inferences on a single set of estimates. >> >> Can anyone suggest an approach to combine estimates from models (2)-(4), >> perhaps through weighted averaging, to generate estimates for the model >> presented in (1) above? An approach is not immediately clear to me since >> the group effects are subsumed in the intercepts in (2)-(4), whereas (1) >> includes separate estimates of group effects instead of a single weighted >> average. >> >> Regards, >> >> Jim >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.