[R] Possible with lm() or glm()?

[John Vokey]

HelpeRs,

I have what amounts to a stupid design---entirely my fault, but it  
seemed like a good idea at the time.  Every participant in the  
experiment receives a series of a two-alternative forced-choice  
(2afc) pairs generated at random from a pool of items that make up  
the pairs.  The independent variable of interest is the difference in  
the 2afc pairs on a metric measure to predict 2afc accuracy.  For  
each participant, the 2afc pairs are generated at random (without  
replacement) from set of items.  This approach means that for any  
participant there are 40 2afc trials in which the independent  
variable varies at random from differences of 0 to some large  
absolute difference.  The problem is that that because of the random  
construction of the pairs there is no consistent variation in the IV  
over participants (e.g., some participants contribute a high  
frequency of judgements on small difference pairs, others on large  
difference pairs, etc.).  What I want is the relationship between the  
IV (pair differences on the metric measure) and 2afc accuracy, with  
the variation due to the random assignment of pair differences to  
participants removed.  To do so, I want to compute the residuals  
relating IV differences to 2afc accuracy after participant variation  
(relating to the covariation with absolute difference) is removed.   
So, I don't want to remove participant variation per se, bit only  
that arising from the covariation of participant with IV pair  
difference.  Ultimately, I want to plot the psychometric function,  
complete with some variant of confidence intervals.  I hope that is  
clear.  So, is there some approach  within lm() or glm() that will  
provide for this?  Any ideas?  To make it concrete, imagine I have a  
data frame consisting of participant as a factor, absolute pair  
difference within participant as the vector IV and response (correct/ 
incorrect) as the vector DV.  [The actual design is more complex, but  
this simple structure captures my concern.]

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-Dr. John R. Vokey