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?






________________________________
From: Ben Bolker <bbolker@gmail.com>
To: r-help@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> 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-models@r-project.org

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