Hi Andrew, Thanks a lot, That would give me what I want. But using my own data and models resulted in this:
> plot(fitted(tcos31.c.cp, level=1), FCR.c$g.cp) Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ This is quite correct, as there are some missing values in the covariate and I made the model using the 'na.action=na.omit' option. I know there is a way of using the model to fix this, but havenĀ“t been able to get the code right during the afternoon. How do I code this and where should I have looked? Cheers /CG On Thu, September 7, 2006 12:03 pm, Andrew Robinson said: > Hi CG, > > I think that the best pair of summary plots are > > 1) the fitted values without random effects against the observed > response variable, and > > 2) fitted values with random effects against the observed response > variable. > > The first plot gives a summary of the overall quality of the fixed > effects of the model, the second gives a summary of the overall > quality of the fixed effects and random effects of the model. > > eg > > fm1 <- lme(distance ~ age, data = Orthodont) > > plot(fitted(fm1, level=0), Orthodont$distance) > abline(0, 1, col="red") > > plot(fitted(fm1, level=1), Orthodont$distance) > abline(0, 1, col="red") > > I hope that this helps. > > Andrew > > On Thu, Sep 07, 2006 at 11:35:40AM +0200, CG Pettersson wrote: >> 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 >> [EMAIL PROTECTED] >> >> ______________________________________________ >> R-help@stat.math.ethz.ch mailing list >> 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. > > -- > Andrew Robinson > Department of Mathematics and Statistics Tel: +61-3-8344-9763 > University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 > Email: [EMAIL PROTECTED] http://www.ms.unimelb.edu.au > -- CG Pettersson, MSci, PhD Stud. Swedish University of Agricultural Sciences (SLU) Dep. of Crop Production Ekology. Box 7043. SE-750 07 Uppsala, Sweden [EMAIL PROTECTED] ______________________________________________ R-help@stat.math.ethz.ch mailing list 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.