Dear statistics and R experts,
I am a new R-user and my statistics is probably more than a bit rusty. So
forgive me if the following question is relatively simple.
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. 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 am fitting the model using something like: fit.rq <- rq(growth ~
ht*spp*light, tau = 0.9)
Unfortunately, there does not seem to be a predict method for rq objects
(unless I am missing something?). I can plot the predicted values by using
lines(x, fit), and I can extract the coefficients from fit.rq using
fit.rq$coef, creating the correct coefficients for a particular level of
spp*light as: intercept <- fit.rq$coef[1,1] + fit.rq$coef[3,1] + ...; slope
<- fit.rq$coef[2,1] + fit.rq$coef[5,1] ...) but this seems very 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.
So, my question is, 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 Landis
R. Matthew Landis, Ph.D.
Dept. Biology
Middlebury College
Middlebury VT 05753
tel. 802/443.3484
fax.802/443.2072
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