Dear statistics and R experts,
 
I'm reposting the following message - I orginally posted it last Friday, and
generated exactly no response.  I assume it got lost over the weekend
(either that or it is just unbelievably obvious!).
 
I would like to plot the predicted values from a quantile regression
analysis (quantreg v.3.31; R v.1.7.1) so that I can evaluate the fit.  
 
I fit the model using something like:  fit.rq <- rq(growth ~ ht*spp*light,
tau = 0.9)
 
My response variable is tree growth (continuous), and my predictor variables
are height (continuous), species (factor with 3 levels), and light (ordered
factor with 3 levels).  I'd like to look at the relationship between growth
and height separately for each combination of species and light.  
 
I would simply use 'predict()' as in lm, but unfortunately, there does not
seem to be a predict method for rq objects (unless I am missing something?).
I know how to extract coefficients from the rq object, and I've calculated
predicted values by simply adding the relevant coefficients for a particular
level of spp*light as:  intercept <- fit.rq$coef[1,1] + fit.rq$coef[3,1] +
...; slope <- fit.rq$coef[2,1] + fit.rq$coef[5,1] +...)  but this is slow
and awkward to do for each of the 9 levels (not to mention different values
of tau).  Plus, I would eventually like to do some non-linear fits, and then
it will be even worse.  I'm sure there must be a way to do this with a
matrix of coefficients, if only my poor memory of linear algebra didn't
prevent me from seeing it.
 
Is there a simple straightforward way to generate the predicted values
without having to manually add up all the relevant coefficients for each
level?  Or, even better, is predict.rq out there somewhere that I haven't
found?  The help on rq objects does refer to it, but ?predict.rq doesn't
turn up anything.
 
Thanks for any help,
Matt
 
R. Matthew Landis, Ph.D.
Dept. Biology
Middlebury College
Middlebury VT 05753
 
tel. 802/443.3484
fax.802/443.2072
 

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