[R] plot(aModel) vs. influence.measures()

[Schwab,Wilhelm K]

A while back I asked about getting a list of points that R considers influential after fitting a linear model, and very quickly got a helpful pointer to influence.measures().  But "it has happened again."  The trouble I am having is that points marked on plots are not flagged in the output from influence.measures(), and I can't read them on the plots.  I tried some successive deletion, but then other points (naturally) start to look troublesome).

Is there a good way to get a list of suspicious entries at the beginning?  In this case, I am trying to help identify possible data entry errors, and I am interested in knowing what R bothered to mark up front.  Perhaps the defaults should be telling me that what I want to do is silly, but it sure _seems_ like it would be helpful.  Is there a way to control the threshold used by influence.measures() to get it to flag more items at one time?  I am learning the hard way, so feel free to tell me that I should be trying to do this some other way.

Bill