On 10-09-02 02:26 PM, James Nead wrote: > My apologies - I have made this more confusing than it needs to be. > > I had microarray gene expression data which I want to use for > classification algorithms. However, I want to 'adjust' the data for > all confounding factors (such as age, experiment number etc.), before > I could use the data as input for the classification algorithms. Since > the phenotype is known to be effected by age, I thought that this > would be a fixed effect whereas something like 'beadchip' would be a > random effect. > > Should I be looking at something else for this? >
Sounds to me as though you should use residuals() rather than fitted() if you want to "adjust for confounding factors". But since you've made up a nice small example, I think you should look at the results of fitted() and residuals() for your example and see if it's doing what you want. > > > ------------------------------------------------------------------------ > *From:* Ben Bolker <bbol...@gmail.com> > *To:* r-h...@stat.math.ethz.ch > *Sent:* Thu, September 2, 2010 2:06:47 PM > *Subject:* Re: [R] Linear models (lme4) - basic question > > James Nead <james_nead <at> yahoo.com <http://yahoo.com>> writes: > > > > > Sorry, forgot to mention that the processed data will be used as > input for a > > classification algorithm. So, I need to adjust for known effects > before I can > > use the data. > > > > > I am trying to adjust raw data for both fixed and mixed effects. > > The data that I > > > output should account for these effects, so that I can use > > the adjusted data > > >for > > > further analysis. > > > > > > For example, if I have the blood sugar levels for 30 patients, > > and I know that > > > 'weight' is a fixed effect and that 'height' is a random effect, > > what I'd want > > > as output is blood sugar levels that have been adjusted for these > effects. > > What's not clear to me is what you mean by 'adjusted for'. > fitted(lm.adj) will give predicted values based on the height > and weight. I don't really know what the justification for/meaning > of the adjustment is, so I don't know whether you want to predict > on the basis of the heights, or whether you want to get a > 'population-level' > prediction, i.e. one with height effects set to zero. Maybe you want > residuals(lm.adj) ...? > > I suggest that follow-ups go to r-sig-mixed-mod...@r-project.org > <mailto:r-sig-mixed-mod...@r-project.org> > > ______________________________________________ > R-help@r-project.org <mailto:R-help@r-project.org> 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
