[R] Model vs. Observed for a lme() regression fit using two variables

[CG Pettersson]

Dear all.

R 2.3.1, W2k.

I am working with a field trial series where, for the moment, I do 
regressions using more than one covariate to explain the protein levels 
in malting barley.

To do this I use lme() and a mixed call, structured by both experiment 
(trial) and repetition in each experiment (block). Everything works 
fine, resulting in nice working linear models using two covariates. But 
how do I visualize this in an efficient and clear way?

What I want is something like the standard output from all multivariate 
tools I have worked with (Observed vs. Predicted) with the least square 
line in the middle. It is naturally possible to plot each covariate 
separate, and also to use the 3d- sqatterplot in Rcmdr to plot both at 
the same time, but I want a plain 2d plot.

Who has made a plotting method for this and where do I find it?
Or am I missing something obvious here, that this plot is easy to 
achieve without any ready made methods?

Cheers
/CG

-- 
CG Pettersson, MSci, PhD Stud.
Swedish University of Agricultural Sciences (SLU)
Dept. of Crop Production Ecology. Box 7043.
SE-750 07 UPPSALA, Sweden.
+46 18 671428, +46 70 3306685
cg.pettersson at vpe.slu.se