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

From: CG Pettersson <cg.pettersson_at_vpe.slu.se>
Date: Thu 07 Sep 2006 - 09:35:40 GMT

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?


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

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Received on Thu Sep 07 19:42:18 2006

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